scenario questions

A company needs to extract key fields from scanned invoices, such as invoice number and total amount, with high accuracy. They want a managed service and plan to use human review for low-confidence results. Which combination of services should they use?

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Your team has deployed a scikit-learn model using a custom container on Vertex AI Prediction. The model receives about 100 requests per second, and the endpoint is configured with a single n1-standard-4 machine. You notice that response times are around 200 ms on average, but occasionally spike to over 10 seconds during traffic bursts. You have set the min replicas to 1 and max replicas to 10. Despite this, spikes still occur. What is the most likely cause and the best course of action?

A company uses BigQuery ML with a remote model calling Vertex AI's pre-trained image classification model. They need to classify images stored in Cloud Storage buckets. What is the correct approach?

A team uses Vertex AI Explainable AI with integrated gradients for a deep learning model. They want to reduce the computational cost of explanations without significantly reducing explanation quality. Which configuration change should they make?

Drag and drop the steps to implement a CI/CD pipeline for ML models using Cloud Build and Vertex AI in the correct order.

You are configuring a Vertex AI Feature Store online store for a real-time recommendation system that requires single-digit millisecond latency and high throughput. The feature values are updated frequently. Which online store type should you use?

You are tasked with building a robust ML pipeline that must be idempotent and handle data skew between training and serving. Which three practices should you implement?

You are deploying a PyTorch model for online predictions on Vertex AI. The model expects input tensors and performs GPU-accelerated inference. You want to minimize prediction latency and maximize throughput. Which approach should you use?

A company has a TensorFlow model that requires GPU for inference. They are deploying on Vertex AI. Which TWO configurations are necessary to ensure GPU is used?

A data scientist has deployed a model on Vertex AI Endpoints and wants to monitor the model's predictions for any drift over time. Which Vertex AI service should they use?

A data engineer wants to use BigQuery ML to train a model that predicts customer churn using a table with customer features and a label column. They want to use a deep neural network. Which model type should they specify?

You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?

A company trains models using Vertex AI Training and wants to share the resulting model artifacts with a different team in another Google Cloud project. What is the most secure way to grant access?

Refer to the exhibit. A team runs this Vertex AI Pipeline definition but the deploy component never executes, even though the evaluate step outputs a metric of 0.9. What is the most likely cause?

A data science team has trained a TensorFlow model and wants to serve it online with minimal latency. Which Vertex AI deployment option should they use to ensure the model can handle traffic spikes without manual scaling?

A pipeline uses the Google Cloud Pipeline Components to perform AutoML training and batch prediction. Which two components from the GCPC library should they use? (Choose two.)

A company is deploying a model on Vertex AI for online predictions with strict latency SLOs. The model requires GPU acceleration. Which TWO configurations should they consider to meet the SLOs while optimizing cost?

Which TWO actions can help reduce prediction latency for a model deployed on Vertex AI Endpoint without changing the model architecture?

A company uses Vertex AI Pipelines with prebuilt components for data processing, training, and deployment. They need to integrate a custom validation step written in Python. What is the correct way to include this as a component?

A team uses Cloud Composer to orchestrate a complex ML pipeline with many tasks. They notice that the DAG parsing time is very high, causing delays in task scheduling. Which action would most effectively reduce DAG parsing time?

An organization uses Cloud Dataflow to preprocess training data. Dataflow jobs are often failing because of insufficient quota for certain resources. The team has requested a quota increase, but the jobs still fail with 'quota exceeded' errors for a different resource. They want to proactively monitor and manage quotas to avoid failures. What is the best approach?

A data analyst wants to build a binary classification model to predict customer churn using SQL queries in BigQuery. Which BigQuery ML model type should they use?

Which of the following is a benefit of using Vertex AI Endpoints with autoscaling and scale-to-zero?

Your Vertex AI custom training job is failing with an out-of-memory error on a single GPU. You need to reduce memory usage without changing the model architecture. Which approach should you try first?

A company stores training data in Cloud Storage and uses Vertex AI Training for model training. They want to implement a data validation pipeline to detect data drift before retraining. Which service should they use?

An ML engineer wants to monitor a deployed model for fairness across different age groups and genders. Which TWO Vertex AI services should they use together to achieve this? (Choose two.)

You are using Vertex AI batch prediction and your model requires preprocessing that involves joining two BigQuery tables. The preprocessing logic is complex and must be done before inference. How should you design the pipeline?

A financial services company has deployed a classification model on Vertex AI to detect fraudulent transactions. The model is monitored using Vertex AI Model Monitoring for skew and drift detection, and also logs predictions to BigQuery for analysis. After a month, the monitoring alerts show a significant drift in one feature (transaction_amount). Which TWO actions should the team take to diagnose and address this issue?

What is the primary purpose of Vertex AI Model Optimization (formerly Model Garden)?

A company wants to analyze customer reviews for sentiment (positive, negative, neutral) using a pre-trained model with no training. They have text data stored in BigQuery. Which Google Cloud service should they use?

An MLOps team has deployed a model on Vertex AI Endpoints and wants to monitor for skew between training and serving data distributions. Which Vertex AI service should they use?

A company is using AutoML Tables to build a fraud detection model. The dataset has 10 million rows with 100 features, heavily imbalanced (fraud cases 0.1%). They used AutoML Tables with default settings and achieved high precision but very low recall. They need to deploy the model for real-time scoring on a Vertex AI Endpoint. The model will be used by a transaction processing system that requires low latency (<100 ms per prediction) and high throughput. The team is concerned about cost as the endpoint will receive up to 5,000 predictions per second. After deploying the model, they notice that the endpoint's latency occasionally spikes to over 1 second during peak hours. The team wants to optimize both model performance (recall) and serving performance. Which course of action should they take?

Refer to the exhibit. This IAM policy is applied at the project level. What is the effect of the condition?

Which Vertex AI service is best suited for finding similar items in a large dataset based on embedding vectors, such as product recommendations or image similarity search?

Your Vertex AI endpoint is experiencing high latency during traffic spikes. You have set maxReplicas=10 and minReplicas=2. The CPU utilisation target is 60%. During spikes, the endpoint never scales beyond 4 replicas. What is the most likely reason?

Which THREE factors should be considered when choosing between using Vertex AI Endpoints and Cloud Run for model serving? (Choose three.)

Which TWO strategies can help reduce the cost of running ML pipelines on Vertex AI?

A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

The exhibit shows a Cloud Build configuration. An ML engineer wants to automate the deployment of a model to Vertex AI after training. What is missing in this config to successfully deploy the model?

A team needs to quickly create a visual interface for data exploration and model building without writing code. They want to run AutoML jobs and visualize results. Which Google Cloud tool should they use?

A data scientist is creating a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. Which TWO statements about pipeline parameters are correct? (Choose two.)

A data scientist runs a batch prediction job on Vertex AI using a custom container. The job processes a large JSONL file (10 GB) and fails with an out-of-memory error. The machine type is n1-standard-4 (15 GB memory). Which action should be taken to resolve the error while minimizing cost?

Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI and uses BigQuery for feature engineering. Recently, the pipeline has been taking significantly longer to execute. Upon investigation, you find that the BigQuery query for feature extraction is being rerun every time the pipeline runs, even though the underlying data hasn't changed. The pipeline is scheduled to run every hour. You want to reduce cost and execution time without losing the ability to detect data drifts. Which approach should you take?

An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?

A company needs to serve a model for low-frequency inference requests (a few hundred per month) from multiple regions. The priority is simplicity and minimal cost without maintaining infrastructure. Which serving option should they choose?

A company needs to perform real-time similarity search on a dataset of 10 million embedding vectors. They expect low latency (under 10ms) and high throughput. Which index type should they use in Vertex AI Vector Search?

Which algorithm does Vertex AI Model Monitoring use by default to detect feature drift in a categorical feature?

A company needs to serve a large Transformer model (5 GB) with strict latency requirements (< 50 ms) and throughput of 1000 requests per second. The model is in SavedModel format. They are considering deployment options on Google Cloud. Which approach best meets these requirements?

A data science team is using AI Platform for training. They want to track hyperparameters and metrics across multiple experiments. What should they use?

A startup wants to build a product recommendation engine without writing custom training code. They have user-item interaction data stored in BigQuery. Which Google Cloud service should they use?

An ML engineer is using Vertex AI Vizier to tune hyperparameters for a custom training job. The training job takes 2 hours per trial. To speed up the process, the engineer wants to run 10 trials in parallel. What is the correct way to configure parallel trial execution?

An ML engineer is using Cloud Build to trigger a Vertex AI Pipeline on every commit to a repository. The pipeline takes 2 hours. The engineer wants to only run the pipeline when changes are made to specific directories. How can this be achieved?

A data scientist wants to use Vertex AI Pipelines to automate a low-code ML workflow. Which two statements are correct regarding best practices? (Choose TWO.)

An ML engineer wants to monitor the performance of a Vertex AI Endpoint. Which TWO metrics are available in Cloud Monitoring for Vertex AI Endpoints? (Choose 2)

You are deploying a large deep learning model on Vertex AI endpoints. The model requires GPU acceleration and you want to minimize cold-start latency. Which TWO actions should you take? (Choose 2 correct answers)

A company is deploying a machine learning model for real-time fraud detection. The model must respond to requests within 100ms. The model is a TensorFlow model and will be deployed on Google Kubernetes Engine (GKE). Which Google Cloud service should be used to serve the model to minimize latency?

Your organization wants to automate the retraining of a model when new data is available and also on a weekly schedule. Which TWO services would you use together to achieve this? (Choose two.)

A retail company wants to build a recommendation system for their e-commerce website. They have user purchase history and product metadata. Which Google Cloud service is most suitable for building a 'frequently bought together' recommendation model with minimal custom ML development?

Your company uses a custom container for model serving on Vertex AI. After a recent update, the model returns predictions but they are clearly wrong (e.g., negative probabilities for a classification model). The logs show no errors. What is the most likely cause?

You need to run a batch prediction job on Vertex AI using a model that requires custom preprocessing using a Python script. The preprocessing must be applied before inference. Which approach should you use?

A company wants to transcribe audio from customer service calls and then analyze the sentiment of the transcribed text. Which TWO Google Cloud services should they use?

A company wants to automatically retrain their model every night at 2 AM using Vertex AI Pipelines. Which approach should they use to trigger the pipeline on a schedule?

A data scientist trained a custom TensorFlow model using Vertex AI Training and wants to deploy it for online predictions with low latency (<100ms). Which deployment option on Google Cloud is best?

A company deploys a model to Vertex AI Prediction with autoscaling enabled. During a flash sale, traffic spikes 10x, but the endpoint fails to scale fast enough, causing high latency. What is the most likely cause and solution?

A data science team is building a real-time feature engineering pipeline for ML model training and serving. They need to compute features from streaming data, store them for low-latency serving, and ensure consistency between training and serving. Which TWO Google Cloud services should they use?

A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?

Which THREE factors should you consider when deciding between online prediction and batch prediction on Vertex AI?

A data engineer wants to create a BigQuery table snapshot for point-in-time recovery of a critical dataset. The snapshot should be created daily and retained for 30 days. What should they use?

A retail company uses Vertex AI AutoML to train a product recommendation model. They have a dataset of past purchases stored in BigQuery. The data science team wants to iteratively train and improve the model. They need to track which dataset version was used for each model and preserve the exact data for reproducibility. They currently export data to CSV files and store them in Cloud Storage. However, the dataset is updated daily, and they want to ensure that models are trained on a consistent snapshot. What should they do?

A data-processing pipeline using Dataflow needs to incorporate a custom ML prediction step. The team wants to maintain fast processing and minimize latency. What is the optimal approach?

A company deploys a model on Vertex AI Endpoints for real-time inference. They need to minimize latency for prediction requests that are identical to previous requests. Which approach should they use?

A team is using TensorFlow Transform (tf.Transform) to create preprocessing functions that will be used both in training and serving. They want to ensure consistency. Which artifact should they save after analyzing the training data?

A company has a large dataset of 1 million unlabeled images for object detection. They want to use AutoML Vision but need to minimize labeling effort. Which strategy should they use?

A financial services company deploys a model on Vertex AI Endpoints with GPU acceleration. They notice that the p99 latency for predictions has increased from 200ms to 1.2s over the past week. CPU utilisation is low, but GPU utilisation is high. Which action should they take to reduce latency?

A data scientist has deployed a model with Vertex AI Endpoints and enabled request/response logging to BigQuery. They want to compute a confusion matrix over time to monitor model quality. What should they do?

A machine learning model deployed on Vertex AI is returning erroneous predictions. The team needs to investigate the root cause by examining the prediction request and response details. Which Google Cloud tool is best suited for this?

A company deploys a model on Vertex AI Endpoints and configures Vertex AI Model Monitoring with a sampling rate of 0.1 and monitoring frequency of every hour. They notice that the monitoring alert fires only after several hours of drift. What is the most likely cause?

A company is using Vertex AI Pipelines for ML workflows. They want to implement best practices for idempotent components and data passing. Which THREE practices should they adopt?

An ML engineer is designing a CI/CD pipeline for ML models using Cloud Build and Cloud Deploy. They want to automatically test model performance on a validation set before promoting to production. Which step should be included in the CI/CD pipeline?

A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?

A team is scaling a prototype ML model to production on Vertex AI. The model was developed using scikit-learn and requires custom preprocessing. They want to minimize operational overhead and ensure consistency between training and serving. Which approach should they use?

A non-technical user wants to build a binary classification model using Vertex AI. Which UI should they use?

A model deployed on Vertex AI Endpoints returns predictions, but the performance metrics (e.g., AUC) degrade over time. The input data distribution is shifting. The team wants to detect and alert on this drift automatically. Which set of actions should they take?

A company wants to track the cost of their Vertex AI prediction endpoint. They use a custom machine type with 1 n1-standard-4 (4 vCPU, 15 GB memory) and 1 NVIDIA T4 GPU. The endpoint is configured for automatic scaling with min=1, max=5 replicas. Which cost monitoring approach should they use?

A data scientist wants to use AutoML Tables to build a binary classification model for loan default prediction. The dataset has 200 features and 1 million rows, with highly imbalanced classes. Which TWO options should they consider? (Choose 2)

Which TWO strategies help ensure data consistency when multiple teams are contributing features to a shared Vertex AI Feature Store?

A financial institution uses a machine learning model to approve loans. They must monitor for fairness and bias. Which THREE Google Cloud tools or features can help them achieve this? (Choose 3.)

A large enterprise has multiple ML models deployed in production across different regions. They want to implement a centralized monitoring dashboard that tracks key performance indicators such as prediction accuracy, latency, and error rates for all models, with the ability to drill down into individual model versions. Which approach best meets these requirements?

Refer to the exhibit. A data scientist runs this Vertex AI training job code. What will be the outcome?

A financial services company uses BigQuery ML to build a logistic regression model for fraud detection. The model is trained on the last 6 months of transaction data (about 50 million rows). After deployment, the fraud detection team notices a high false positive rate, causing customer dissatisfaction and extra manual review costs. The model is currently retrained monthly. The team wants to reduce false positives without sacrificing recall. They have access to real-time transaction streaming and can compute new features quickly. What is the most effective approach?

A machine learning engineer is designing an ML pipeline on Vertex AI. The pipeline includes multiple steps: data validation, preprocessing, training, evaluation, and deployment. The engineer wants to ensure that if the data validation step fails due to schema mismatch, the pipeline stops immediately and does not proceed. Additionally, they want to reuse the preprocessed data from a previous successful run if the source data hasn't changed. Which two configurations should they use? (Choose two.)

A company uses Cloud Composer to orchestrate an ML pipeline. They notice that the pipeline occasionally fails because the Composer environment runs out of disk space on the worker nodes. The pipeline uses many large dependencies. What is the most effective long-term solution?

A healthcare company uses AutoML Tables to predict patient readmission risk. The dataset contains 500,000 rows and 200 features, including patient demographics, lab results, and medical history. The model accuracy is lower than expected. The engineer wants to improve performance using low-code techniques. Which THREE actions are most effective? (Choose THREE.)

Refer to the exhibit. A Vertex AI prediction endpoint is failing with a deadline exceeded error. The log shows the following. What is the most likely cause?

You are monitoring a classification model that predicts loan default. The model was trained on data from 2020-2022. In 2023, the economic conditions changed, and the model's accuracy dropped significantly. Which monitoring approach would best help you detect this issue early?

Your organization has a requirement to monitor fairness of an ML model that predicts loan approvals. You need to set up alerts if the model's predictions show bias against a protected group. Which tool on Google Cloud can you use to monitor this?

A company uses Vertex AI Pipelines to orchestrate an AutoML tabular training step followed by a BigQuery ML evaluation step. The pipeline fails because the output of the AutoML step (a model resource name) is not being passed to the BigQuery step. What is the most likely cause?

A financial company is building a fraud detection model. The dataset has 1% fraud cases and 99% legitimate transactions. Which technique should they use to handle the class imbalance?

An ML team has set up automated retraining triggered by Cloud Monitoring alerts. When a feature drift alert fires, a Cloud Function publishes to Pub/Sub, which triggers a Vertex AI Pipeline. However, the retraining pipeline is failing because the training data is not updated. What is the most likely cause?

A data analyst wants to use Vision API to detect custom objects in manufacturing images, but the pre-trained API does not recognize their specific components. They have 1000 labeled images. Which path offers the fastest time-to-value with minimal coding?

You need to reduce the cost of training a large model on Vertex AI while maintaining fault tolerance. Which THREE actions should you take? (Choose 3)

A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?

An ML team wants to automatically track training runs, including hyperparameters and metrics, with minimal code changes. Which Vertex AI service should they use?

Refer to the exhibit. A team runs the command above and sees only two models. They know there is a model 'model-v3' created three days ago. What is the most likely reason it is not listed?

A team deployed a prototype classification model to Vertex AI Prediction. After a week, they notice the metrics shown in the exhibit. What is the most likely cause of the performance degradation and latency increase?

A marketing team wants to analyze customer reviews for sentiment without writing code. Which Google Cloud service should they use?

A company has developed a prototype fraud detection model using a small sample of transactions. The prototype runs on a single VM and uses a Random Forest classifier. They want to scale to the full dataset of 50 million transactions. The data is stored in BigQuery. The team wants to use Vertex AI for training. After moving the code to a custom training container and using Vertex AI Training with a single n1-standard-4 machine, the training job fails with an error: "Process terminated with exit code 1". The logs show: "java.lang.OutOfMemoryError: Java heap space". The model uses a scikit-learn RandomForest. Which course of action is most appropriate?

Two teams are collaborating on a project and want to use a shared Feature Store in Vertex AI. They need to ensure that features are discoverable and that access is controlled. What is the best practice?

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

A team has set up the IAM policy above on a Vertex AI project. Alice, a data scientist, reports that she cannot create a Vertex AI Training custom job using a pre-built container. Other data scientists in the group 'data-scientists@example.com' have the same issue. What is the most likely cause?

An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?

A team wants to share a trained model with other teams within the organization. They need to provide access to the model artifact in Vertex AI Model Registry and ensure that only authorized teams can deploy the model. What should they do?

An ML engineer is building a pipeline that includes a step to run a BigQuery query and pass the results to the next step. They want to use a pre-built Google Cloud Pipeline Component for BigQuery. Which component should they use to execute a query and output the results to a destination table?

You are troubleshooting a failed Vertex AI AutoML training pipeline. The error log shows: 'ValueError: budget_milli_node_hours must be greater than 0'. What is the root cause?

You are performing post-training quantisation of a trained TensorFlow model to INT8 for deployment on edge devices. Which technique should you use to minimise accuracy loss?

A retail company wants to implement a recommendation system using Recommendations AI. They need to generate personalized recommendations for users based on their browsing history and purchase behavior. Which THREE recommendation types are available in Recommendations AI?

A company wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in Cloud Storage, and after training, the model should be automatically deployed to a staging endpoint if evaluation metrics pass a threshold. They also need to detect skew between training data and serving data. Which two services should they use for skew detection?

A company has a large-scale ML system that uses Vertex AI Pipelines to retrain models weekly. The pipeline includes a custom training job and a batch prediction step. After moving to production, they observe that batch prediction jobs often fail with 'Quota exceeded' errors. The project has sufficient CPU quota. What is the most likely cause?

A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

An ML engineer is using Vertex AI Pipelines with Kubeflow Pipelines SDK (KFP) to orchestrate a training and deployment workflow. They want to reuse a custom component across multiple pipelines. The component is defined in a Python file 'preprocess.py' that includes a function decorated with @kfp.components.create_component_from_func. How should they package this component for reuse?

You have a Vertex AI endpoint with two deployed models: a champion (v1) and a challenger (v2). You set the traffic split to 90% v1 and 10% v2. After a week, you observe that v2 has better business metrics. You want to shift all traffic to v2 gradually over 3 days to avoid any risk. What should you do?

A data scientist wants to automate the retraining of a model when new data arrives in Cloud Storage. Which Google Cloud service is most appropriate for orchestrating this workflow?

An engineer wants to set up request/response logging for a Vertex AI Endpoint to analyze prediction behavior. Which TWO resources must be configured? (Choose 2)

A company uses Vertex AI Feature Store with an online store for low-latency serving. They observe high latency during peak hours. The feature values are small (< 1 KB each) and the workload is read-heavy. Which change would most effectively reduce latency?

A data scientist is training a very large neural network using Vertex AI with multiple GPUs across multiple nodes. The model does not fit on a single GPU, so they need to use both data parallelism and model parallelism (pipeline parallelism). Which THREE components or configurations are required to set up distributed training with Vertex AI?

Refer to the exhibit. A team leader applies this IAM policy on a Vertex AI model resource. What does the condition accomplish?

A company has an existing TensorFlow model for fraud detection that they want to use for predictions in BigQuery. They want to call the model from SQL queries without moving data out of BigQuery. How should they deploy the model?

A data science team deploys a PyTorch model using Vertex AI Prediction. The model requires GPU for inference, but they notice high costs and underutilized GPUs during off-peak hours. What is the most cost-effective solution?

A machine learning engineer notices that a model served on Vertex AI Endpoints returns predictions that are consistently 20% slower during the first request after idle (cold start). They are using automatic scaling with min replicas=1. What is the most likely cause and best solution?

A company deploys a model to Vertex AI Endpoint with autoscaling enabled. During a traffic spike, they observe high tail latency (99th percentile > 2s). Which TWO factors are most likely contributing to this latency?

You need to run batch predictions on 10 TB of text data stored in BigQuery using a custom container model hosted in Vertex AI. What is the most cost-effective and simple approach?

A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 rows and 50 features, including loan amount, credit score, debt-to-income ratio, and employment length. The target variable is binary: 'default' (1) or 'no default' (0). The data is highly imbalanced, with only 2% defaults. The data scientist trains a model with AutoML Tables using default settings. The evaluation metrics show an AUC of 0.85, but the confusion matrix reveals that the model predicts 'no default' for almost all cases, missing most defaults. The data scientist needs to improve the model's ability to identify defaults without significantly increasing false positives. They have limited time and cannot write custom code. What should they do?

You need to perform batch predictions on 10 TB of data stored in BigQuery using Vertex AI. The model requires some preprocessing that cannot be expressed in SQL. What is the most scalable approach?

A company has deployed a model that predicts customer churn. The model's performance, as measured by AUC, has been declining over the past month. The team suspects data drift. They have enabled Vertex AI Model Monitoring, but no alerts have been triggered. What is a possible reason for the lack of alerts?

An ML engineer is training a very large PyTorch model on Vertex AI using a TPU v3 pod. The training is slower than expected, and the TPU utilization is low. What is the most likely cause?

A team is deploying a model on Vertex AI Prediction. Which THREE configuration settings have a direct impact on both latency and cost? (Choose THREE.)

A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?

An ML engineer needs to monitor the online prediction latency of a Vertex AI Endpoint. Which metrics should they look at in Cloud Monitoring?

You are monitoring a production model that is experiencing gradual decay in AUC. Which THREE metrics should you set up alerts for to diagnose the root cause? (Choose three.)

A company wants to use Document AI to process a large volume of invoices. They need to extract line items and also have a human review the extracted data for accuracy. Which THREE features should they use? (Choose 3)

A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?

A data science team has trained a custom model using Vertex AI and wants to deploy it for online predictions with low latency. Which TWO actions should they take to optimize performance?

A company wants to train a custom machine learning model on Vertex AI using a pre-built container for scikit-learn. They want to use spot VMs to reduce costs. However, the training job fails intermittently due to preemption. Which TWO actions should they take to ensure the training job completes successfully?

A machine learning engineer wants to monitor the fairness of a credit approval model across demographic subgroups. They have ground truth labels in BigQuery. Which approach should they use to evaluate performance disparities?

Refer to the exhibit. An ML engineer in the team needs to deploy the model to an endpoint. The engineer is assigned the 'roles/aiplatform.user' role at the project level but still cannot deploy. What is the most likely reason?

A team is using Cloud Composer to orchestrate ML workflows. They want to allow multiple data scientists to contribute DAGs without interfering with each other. What is the recommended approach?

A media company uses a custom Python script on a Compute Engine VM to run batch predictions with a large ML model. The script loads the model from Cloud Storage, processes records from a Pub/Sub pull subscription, and writes results to BigQuery. Predictions are taking too long and the VM often runs out of memory. Which two changes should the company implement to improve performance and scalability? (Choose TWO)

Drag and drop the steps to set up a distributed training job on Vertex AI using a custom container in the correct order.

A company uses a custom container on Vertex AI Prediction. They want to send custom metrics from their prediction container to Cloud Monitoring. Which method should they use?

A data science team is designing a Vertex AI pipeline that includes a loop over a list of hyperparameter sets. They want to run training jobs in parallel for each hyperparameter set and then collect the results for comparison. Which two Kubeflow Pipelines SDK v2 features should they use? (Choose two.)

A data scientist is using Vertex AI Workbench notebooks and wants to collaborate with team members in real-time on the same notebook. Which notebook type supports real-time collaboration?

A mobile app company needs to run an image classification model on-device for real-time performance. The model is a ResNet-50 trained in TensorFlow. They need to reduce latency to under 50ms on a mid-range phone. Which optimization should they apply first?

Drag and drop the steps to set up a feature store for ML features using Vertex AI Feature Store in the correct order.

You are using Vertex AI Prediction with a custom container that requires a large model file (5 GB). Deployment takes 10 minutes to start. You want to reduce cold start latency. Which action would be MOST effective?

An ML engineer is configuring Vertex AI Model Monitoring for drift detection on a deployed endpoint. Which TWO settings directly affect the frequency and accuracy of drift detection? (Choose 2)

A company is deploying a TensorFlow model on Vertex AI Prediction. The model is memory-intensive and requires GPU acceleration. The team wants to minimize latency and cost. Which TWO configurations should they select? (Select 2)

A financial services company uses a custom deep learning model on Vertex AI to automatically approve or reject credit card transactions. The model is explainable using Vertex Explainable AI, and the company monitors feature attribution drift with thresholds defined per feature. Last week, the monitoring system flagged that the mean absolute attribution score for the 'transaction_amount' feature increased from 0.35 to 0.55. The overall model accuracy, measured on a daily batch of labeled transactions, has remained around 97%. The operations team is concerned about potential compliance issues due to changing model behavior. What should the data scientist do?

A team wants to monitor prediction drift on a Vertex AI Endpoint for a classification model. They have configured Vertex AI Model Monitoring with default settings. Which metric will be used to detect prediction drift?

You are responsible for deploying a real-time recommendation model that uses a large embedding table (5 GB) and a small neural network. The model is served through a custom container on Vertex AI Prediction. The end-to-end latency requirement is under 200 ms. During load testing with 500 QPS, you observe that latency increases linearly with batch size. You are currently using a single replica with an n1-standard-8 machine and one T4 GPU. The embedding table is loaded entirely in GPU memory. However, CPU utilization is at 100% while GPU is at 30%. What is the best approach to meet the latency requirement at scale?

A company runs batch predictions on Vertex AI every hour using a custom container. They want to reduce costs by minimizing idle time while ensuring the batch job completes within 10 minutes. Which endpoint configuration should they use?

A data scientist wants to perform feature engineering on a large dataset stored in BigQuery before training a model. Which feature engineering tool is most appropriate?

A data engineer is using BigQuery ML with a BOOSTED_TREE_CLASSIFIER model. After training, they want to evaluate the model and understand which features contribute most to predictions. Which THREE BigQuery ML functions should they use?

A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?

A machine learning team wants to perform A/B testing between two model versions (v1 and v2) on Vertex AI Endpoint. They need to gradually route 10% of traffic to v2 while monitoring performance. What is the most efficient way to achieve this?

A team has successfully trained a deep learning model on Vertex AI using a custom container and distributed training with TensorFlow. They want to serve this model for online predictions with low latency. They deploy the model to Vertex AI Endpoint with a single n1-standard-4 machine. During load testing, they observe that the median latency is 200ms, but the 99th percentile latency spikes to 2 seconds. The model is a complex neural network that takes variable-length text as input. Which approach will best reduce tail latency while maintaining throughput?

An organisation wants to use Document AI to process contracts but requires human review for high-risk clauses. Which feature should they enable?

Refer to the exhibit. A team member complains they cannot deploy a model to Vertex AI Endpoints. What is the most likely reason?

A data science team needs to share features across multiple ML models while ensuring consistency between training and serving. Which approach best achieves this?

A company wants to use Vertex AI Vizier to tune hyperparameters for a PyTorch model. They have a limited budget of 50 training jobs. The objective metric is validation accuracy, and they want to find the best configuration efficiently. Which algorithm should they choose?

A company uses Delta Lake on Dataproc for their data lake. They need to ensure ACID transactions and schema enforcement for data ingested from streaming sources. Which Delta Lake feature should they enable?

A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?

A company wants to implement a document processing solution that extracts key information from invoices and receipts. They have limited ML expertise and want to use a pre-trained solution as much as possible. Which Google Cloud service should they use?

A company deploys an AutoML Vision model for real-time defect detection. They notice high inference latency during peak hours. Which configuration change can help?

A large e-commerce company uses Vertex AI to train a recommendation model daily. The training pipeline is built with Vertex AI Pipelines and involves three steps: data preprocessing, training, and model evaluation. The pipeline is triggered by a Cloud Scheduler job every morning at 8 AM. Recently, the pipeline has been failing intermittently during the data preprocessing step, with an error message indicating 'ResourceExhausted: Quota limits exceeded for read api requests.' The team has checked and confirmed that the quota for BigQuery read requests is not exceeded at the project level. The preprocessing step reads data from a BigQuery table with billions of rows. The team has also noticed that the pipeline runs on a custom machine type (n1-standard-4) with a persistent disk. What is the most likely cause of this error?

An ML engineer is using Vertex AI for distributed training of a PyTorch model across multiple nodes. The training job must use TPUs for high throughput. The engineer sets up the job configuration. Which THREE components are required for the training to work correctly? (Select 3)

A machine learning engineer is using Vertex AI Pipelines and wants to run a custom Python function as a component. They need to pass a dataset artifact from a previous component and output a model artifact. Which decorator should they use to define the component in the Kubeflow Pipelines SDK v2?

A company wants to use DVC for data versioning alongside their ML code in Git. Which TWO statements about DVC are correct? (Select 2)

A machine learning team is training a large transformer model on Vertex AI. They need to reduce training time by utilizing multiple GPUs across nodes, but the model is too large to fit into a single GPU memory. Which distributed training strategy should they use?

A company wants to implement a CI/CD pipeline for their ML models using Vertex AI. They need to automatically retrain the model when new data arrives, but only if the model performance on a validation set has degraded by more than 5% compared to the current production model. Which three services or components should they incorporate into the automated pipeline? (Choose three.)

A company uses Vertex AI Pipelines for ML workflows. They want to standardize pipeline templates across teams to ensure consistency. Which TWO approaches should they use?

A data science team is using a shared Cloud Storage bucket to store training data. Multiple team members are simultaneously uploading new data files, and occasionally the wrong version of a file is used in training, leading to inconsistent results. Which best practice should the team implement to ensure data version consistency?

A machine learning engineer needs to share a trained model with the product team for integration. The model is stored in Cloud Storage, and the product team’s service account needs read access. The engineer wants to follow the principle of least privilege. Which IAM configuration should be used?

An ML engineer wants to use Vertex AI Model Garden to deploy a pre-trained foundation model for text summarisation. What is the quickest way to achieve this?

A healthcare startup is using Vertex AI to train a deep learning model for detecting anomalies in chest X-rays. The training dataset is 500 GB of images stored in Cloud Storage (GCS). They use a custom training container with TPU v3-32. The training job completes successfully, but the model performance is poor. On investigation, they discover that the input images were not preprocessed correctly: the images were resized to 256x256 instead of the required 512x512. They need to fix the preprocessing and retrain as quickly as possible. The preprocessing pipeline involves decompressing, resizing, normalizing, and augmenting images. They have a small team and limited time. Which approach should they take?

Your team has a production ML model on Vertex AI that shows a gradual decline in accuracy over the past week. The model is retrained weekly using the latest data. Which monitoring approach should you implement to detect the issue earlier?

A team has a trained TensorFlow model running locally and wants to deploy it for low-latency online predictions on Google Cloud. Which service should they use?

You are fine-tuning a pre-trained BERT model from Hugging Face for a sentiment analysis task using Vertex AI training. The dataset has 100k examples. To avoid catastrophic forgetting, which layer freezing strategy should you apply?

A company has multiple teams that need to access and manage ML models in Vertex AI. Different teams require different permission levels: the data science team should be able to create and update models, while the MLOps team should have full control. What is the recommended approach to manage access?

After deploying a new version of a model to a Vertex AI Endpoint, the team notices that predictions are still returning results from the old version. The deployment command used a traffic split of 100% to the new version. What is the most likely cause?

You have a Vertex AI endpoint that serves a model for real-time predictions. You want to update the model to a new version with zero downtime. Which approach should you take?

A company is evaluating Google Cloud ML solutions. Which TWO services are appropriate for building custom machine learning models (not using pre-built APIs)? (Choose TWO.)

You are deploying a model for real-time inference with strict latency requirements (<100ms P99). You want to autoscale based on custom metrics. Which TWO actions should you take? (Choose 2)

A company is deploying a complex model that requires GPU for inference. They want to use Vertex AI for serving. Which TWO steps are required to deploy the model with GPU support? (Choose 2)

A company needs to classify images of products into categories (e.g., electronics, clothing, food). They have labeled images and want to use a low-code solution on Google Cloud. Which service is suitable for this task?

A team is training a model using historical data and wants to avoid data leakage when joining feature values from a feature store. The features include time-varying data like user activity counts. Which retrieval method should they use when creating a training dataset?

An ML engineer is building a pipeline component that takes a dataset URI and a model URI as inputs, and outputs a classification metrics artifact. Which KFP SDK v2 type should the output artifact be annotated with?

A company uses AutoML Tables (Vertex AI AutoML for tabular data) to predict customer churn. Their dataset has 10,000 rows and 50 features. During training, they notice the model's performance is poor. Which approach is most likely to improve the model?

An organization runs a Vertex AI pipeline that includes a model evaluation step. Team members want to reuse previously computed evaluation metrics when re-running the pipeline with unchanged code and hyperparameters. Which feature should they enable?

Which TWO are benefits of using Vertex AI Pipelines for ML workflow orchestration over deploying custom Airflow DAGs in Cloud Composer? (Choose TWO.)

A data engineer is setting up a data pipeline for ML training. The raw data is in Cloud Storage, and they need to transform it into features stored in Vertex AI Feature Store. The pipeline should run daily. Which service should they use?

You are deploying a PyTorch model on Vertex AI using a custom container with NVIDIA Triton Inference Server. The model is a large transformer that requires GPU. You want to optimize GPU utilization and reduce memory footprint. Which technique should you apply?

A company uses Vertex AI Model Monitoring to detect training-serving skew. They have a categorical feature 'product_category' with high cardinality. The monitoring job alerts for skew, but the data scientists believe the model performance is still acceptable. Which THREE actions should the team take to investigate and resolve the alert?

An ML team is using Vertex AI Pipelines to automate model training and deployment. They want to reuse components across multiple pipelines. What is the best practice for managing component code?

A machine learning pipeline includes a conditional branch: if model accuracy exceeds 0.95, deploy to production; otherwise, send a notification. Which KFP SDK feature allows implementing this logic within the pipeline definition?

A team has a pipeline that trains a model and then evaluates it. They want to conditionally deploy the model to a staging endpoint only if evaluation metrics exceed a threshold. Which KFP feature should they use?

The pipeline fails during the evaluate component with error "Model not found". What is the most likely cause?

A company uses Vertex AI for training. They have a large dataset stored in Cloud Storage and need to train a custom model using TensorFlow. The training job is failing with an out-of-memory error. What is the best first step?

Which THREE factors should be considered when choosing a compute option for serving a deep learning model in production on Google Cloud? (Choose three.)

A company wants to automatically retrain their model when data drift is detected. Which THREE components are needed to implement this pipeline?

A large e-commerce company uses Vertex AI Pipelines to orchestrate its recommendation model training. The pipeline has several parallel components: feature engineering, model training, and model evaluation. Recently, they noticed that the pipeline often fails due to resource exhaustion in the Vertex AI custom training job for the model training component. The training job consumes significant memory and occasionally exceeds the allocated memory limit, causing the pod to be OOMKilled. The team has already increased the memory to the maximum allowed for the chosen machine type. They need to prevent the pipeline from failing while still using the same machine type. Which approach should they take?

Which THREE components should you include in a comprehensive model monitoring dashboard for a production ML system?

A model deployed on a Vertex AI Endpoint uses an image model with XRAI explainability. The team notices that the prediction distributions are shifting over time. They want to monitor prediction drift. However, the explainability feature is not enabled. What must the engineer do to enable monitoring prediction drift?

Your team is deploying a large recommendation model on Vertex AI endpoints using GPUs. You need to minimise latency while optimising cost. The model serves many similar requests from the same users within short time windows. Which additional service would best reduce latency and cost?

An e-commerce company uses a Vertex AI endpoint for product recommendations. Recently, the click-through rate (CTR) dropped significantly. Model monitoring shows no significant data drift or skew. Logs show increased latency but no errors. Which technique should the engineer use to diagnose the issue?

A healthcare startup is developing a diagnostic model using sensitive patient data. They use Vertex AI to manage the training pipeline. They need to ensure that the data is encrypted both at rest and in transit. Additionally, they want to prevent the ML engineers from seeing raw data but still allow them to train models. They use Cloud Storage with CMEK and VPC-SC. They plan to use Vertex AI Training with a custom service account. The data stored in Cloud Storage is encrypted with CMEK. What additional step is needed to allow Vertex AI Training to access the encrypted data?

A retail company wants to forecast weekly sales for each of its 500 stores. The data includes historical sales, promotions, holidays, and local weather. The company needs to update forecasts every week with new data. Which ML approach should they use?

You are using Vertex AI continuous evaluation (model monitoring) for your deployed model. You receive an alert that the prediction distribution is significantly different from the training distribution. What should you do first?

A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?

A company wants to monitor features in Vertex AI Feature Store for drift over time. Which two services should they use? (Choose two.)

A data engineering team uses Dataflow for preprocessing and wants to integrate with Vertex AI Pipelines. They need to pass the preprocessed data location to the training step. What is the best practice?

A company is deploying a model for online predictions on Vertex AI. They want to minimize latency while also handling traffic spikes. Which TWO configurations should they choose?

A data scientist deployed a TensorFlow model for sentiment analysis to Vertex AI Prediction. The model expects input key 'text' but the client sends requests with key 'review_text'. Which step should the data scientist take to resolve the error without retraining the model?

An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?

A data scientist deployed a model to Vertex AI Prediction. When making a prediction request as shown in the exhibit, they receive a 400 error. What is the most likely cause?

Your team manages a production ML pipeline on Google Cloud that trains a fraud detection model every 6 hours using new transaction data. The pipeline steps are: (1) Cloud Function triggered by new files in Cloud Storage to validate data, (2) Dataflow job for feature engineering, (3) Vertex AI CustomJob for training, (4) Cloud Function to deploy the model to a Vertex AI endpoint after evaluation. You notice that the pipeline sometimes fails during the Dataflow job step with an error: 'Workflow failed. Causes: The job encountered a system error. Please try again later.' The error occurs sporadically, and retrying the pipeline manually usually succeeds. The team needs a reliable automated solution. What should you do?

A company wants to cache predictions for identical requests to reduce latency and cost. They use Vertex AI Prediction with a custom container. Which GCP service should they use to implement prediction caching?

A retail company wants to build a customer churn prediction model using AutoML Tables. They have a dataset with 5000 rows and 50 features, including customer ID, transaction history, and support tickets. The target is a binary column 'churned'. After training, the model shows high accuracy but low recall for the churned class. What is the most likely cause?

An ML team is converting a prototype model to a production pipeline using Vertex AI. They want to ensure model versioning and lineage. Which two practices should they adopt? (Select TWO)

A team prototypes a recommendation model using a Jupyter notebook on Vertex AI Workbench. They want to productionize the model with CI/CD. Which approach should they use to package the model for deployment?

A team of data scientists and ML engineers is collaborating on a project using Vertex AI Workbench. They need to share notebooks and code, but want to avoid conflicts and maintain a history of changes. Which approach should they use?

The exhibit shows a Vertex AI PipelineJob submission command. The pipeline fails because the component cannot find the input data. What is the most likely cause?

A team uses Vertex AI Feature Store with an online store for low-latency serving. They need to support frequent updates to features (e.g., every minute) and require high write throughput (thousands of writes per second). Which online store type should they choose?

An ML team wants to monitor feature drift in their production model. Which Vertex AI Feature Store capability should they use?

Refer to the exhibit. A team configured Vertex AI Model Monitoring with skew detection for feature "income" with a threshold of 0.2. However, they have not received any alerts even though they suspect data drift. What is the most likely reason?

An ML team uses Vertex AI Pipelines and wants to automatically generate model cards documenting model purpose, evaluation results, and intended use. Which approach should they take?

A team trained a TensorFlow model locally and wants to deploy it to BigQuery ML for predictions without retraining. They have exported the SavedModel to Cloud Storage. Which statement is correct?

A company uses Vertex AI Matching Engine for real-time recommendations. They need to serve queries with low latency and support frequent updates. Which two configurations are appropriate? (Choose 2)

A data science team deploys a custom container on Vertex AI Prediction for a PyTorch model. After deployment, the model returns predictions that are consistently off by a constant factor. The model performed correctly during local testing. What is the most likely cause?

A data scientist wants to perform A/B testing between two model versions deployed on the same Vertex AI endpoint. They need to route 10% of traffic to the challenger model. Which approach should they use?

A company wants to set up end-to-end monitoring for a Vertex AI model. Which three components should they include?

An organization runs a batch prediction job on Vertex AI for a large dataset (10 TB). The job is configured to use a cluster of 100 n1-standard-16 machines. Midway through, the job fails with 'Out of memory' errors. What is the most effective mitigation strategy?

A company has deployed a model to Vertex AI Endpoints and wants to monitor for feature drift using Jensen-Shannon divergence. They have set a threshold of 0.1. After one week, the monitoring job reports a divergence of 0.15 for a feature. What should the engineer do next to diagnose which features are contributing to the drift?

Which THREE of the following are supported output types for BigQuery ML?

You are a Machine Learning Engineer at a financial services company. You have trained a large language model (LLM) using a custom container on Vertex AI Training. The model is used for sentiment analysis on financial news articles. You have deployed the model to a Vertex AI Endpoint for online prediction. However, during peak trading hours, users report high latency ( > 5 seconds) and occasional timeout errors. The model is deployed on n1-highmem-8 machines with 1 replica. You monitor the endpoint and see that CPU utilization is high ( > 90%) and memory is near capacity. The queries are relatively small text inputs. Which course of action should you take to reduce latency?

Your team is deploying a large model on edge devices and needs to reduce its size by 80% while maintaining reasonable accuracy. Which THREE techniques should they consider? (Choose 3.)

An ML engineer is using Cloud Composer (Airflow) to orchestrate a ML workflow. They need to run a Vertex AI pipeline as one of the tasks in the DAG. Which Airflow operator should they use?

A data scientist wants to train a PyTorch model on Vertex AI using a pre-built container for GPU training. She needs to use 4 NVIDIA A100 GPUs on a single machine. Which machine configuration should she select?

Which Vertex AI service is designed for building and managing approximate nearest neighbor (ANN) indexes for similarity search at scale?

An e-commerce company deployed a Vertex AI AutoML Tables model to predict customer churn. The model is served via a private endpoint with a dedicated machine type n1-standard-4. After a week, they observe that 5% of predictions fail with 'Request timed out' error. The average prediction time is 1.2 seconds but spikes to 4 seconds during peak hours. The input data is 50 features. They have enabled autoscaling with a min node count of 1 and max of 5. Which action is most likely to resolve the timeout issue without increasing complexity?

Refer to the exhibit. A machine learning engineer deployed a model on Vertex AI using this configuration. When testing the endpoint, the engineer receives a 400 error with the message: 'Invalid argument: Explanation metadata missing required field: `outputs`.' What is the most likely cause?

A data analyst wants to create a classification model directly in BigQuery using SQL. Which feature should they use?

A data science team deploys a large language model (LLM) on Vertex AI Prediction using an NVIDIA A100 GPU. The end-to-end latency is acceptable, but the cost is high due to low GPU utilization. The model is stateless and requests are independent. Which strategy would most effectively reduce cost per prediction?

A retail company uses Recommendations AI to power personalized product recommendations on their website. They notice that the 'frequently-bought-together' model is not capturing complementary items that are often purchased in the same session but not necessarily in the same transaction. Which TWO actions should they take to improve the model?

A data scientist is training a model using Vertex AI Experiments and wants to automatically log model parameters, metrics, and artifacts without modifying their training script. Which approach should they use?

A team wants to implement CI/CD for their ML pipeline using Cloud Build. They want to automatically compile and deploy the pipeline when code is pushed to the main branch. Which three steps should they include in the Cloud Build configuration? (Choose three.)

An engineer is designing a distributed training job on Vertex AI for a TensorFlow model that uses the MultiWorkerMirroredStrategy. They need to ensure proper communication between workers. Which environment variable must be set correctly for each worker?

A machine learning engineer wants to monitor model performance on Vertex AI for a regression model. Which metric is most appropriate to track the average prediction error?

A data science team has trained a large deep learning model using Vertex AI Workbench. They want to deploy it to Vertex AI Prediction for online serving. The model is stored in a custom container with a Python-based web server. Which TWO actions should the team take to ensure optimal performance and cost?

A company deploys a custom TensorFlow model to Vertex AI Endpoint for online predictions. After deployment, prediction latency is consistently high (over 500ms) even under low traffic. The model is CPU-only and the default machine type (n1-standard-2) is used. Which action will most likely reduce prediction latency?

Which TWO options can help detect model performance degradation in production? (Choose two.)

A data science team is using Vertex AI Feature Store for online serving. They notice that the online serving latency is high. What is the most likely cause?

A company trains a model using Vertex AI Training and then deploys it to Vertex AI Prediction. They notice that prediction requests fail with 'InvalidArgument: input tensor shape mismatch'. Which THREE are possible causes?

A data scientist wants to automatically generate model documentation that includes model purpose, training data, evaluation results, and intended use. Which tool should they use?

Refer to the exhibit. A data engineer is defining a Vertex AI Pipeline step to train a model. The pipeline fails with an error: "Failed to create vertex ai custom job: Invalid resource name." What is the most likely cause of the error?

Your team manages multiple ML models in Vertex AI Model Registry. Each model has several versions deployed to different endpoints for testing and production. You need to implement a process where a model version can be promoted from a staging environment to production only after it has passed automated validation tests and been approved by a designated reviewer. The team uses CI/CD pipelines (Cloud Build) for training and deployment. Currently, model versions are deployed to endpoints using Vertex AI Endpoints with a single traffic split configuration. You want to track promotion requests and enforce approval gates. What should you do?

Refer to the exhibit. The team wants to automatically deploy the best-performing model version to production. They have set up a Cloud Function triggered by Model Registry events. Which alias should they use in the function to get the latest champion?

A data analyst wants to train a binary classification model in BigQuery ML on a dataset of 10 million rows with 50 features. They need to evaluate the model's performance on a held-out test set. Which sequence of SQL statements should they run?

A team is using AI Platform Data Labeling Service to label data for a classification model. They want to allow a labeler from a different team to work on the same dataset. What is the correct way to grant access?

Match each model evaluation metric to its use case.

You need to serve a TensorFlow model that has a cold start latency of 20 seconds. The model is used for a real-time application with unpredictable traffic, but occasional bursts require immediate responses. What is the best deployment strategy to minimize both cold start impact and cost?

Which THREE considerations are important when setting up a shared feature store in Vertex AI Feature Store for multiple teams?

A healthcare organization wants to build a model to predict patient readmission risk using structured electronic health record (EHR) data. They need to train a model using SQL in BigQuery, but they also want to leverage AutoML's ability to automatically search for the best architecture. Which approach should they take?

A manufacturing company uses AutoML Tables to predict equipment failure. They want to improve model performance without increasing manual effort. Which three actions should they take? (Choose THREE.)

Which TWO practices are important when scaling a prototype ML model to production on Google Cloud? (Choose two.)

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

A global retailer has deployed a real-time product recommendation model on Vertex AI Endpoints. The model is a large neural network that runs on a single node with 8 vCPUs and 30 GB memory. Over the past week, the p99 latency has increased from 200ms to 2 seconds, and the error rate has risen to 5%. Cloud Monitoring shows that the endpoint's CPU utilization is consistently near 100%, and memory is at 80%. The ML engineer suspects the model is too large for the node, but model size has not changed. Logs show no increase in request volume (steady at 50 QPS). There are no recent model updates. The engineer has tried to increase the node to 16 vCPUs, but latency decreased only slightly. What is the most likely root cause and the best first step to resolve it?

A media company wants to automatically moderate user-uploaded videos by detecting explicit content (e.g., violence, adult material). They need a solution that integrates with their video processing pipeline and scales to millions of videos. Which approach should they take?

A company wants to implement a retraining trigger for their ML model. They have set up Cloud Monitoring alerts that fire when drift exceeds a threshold. What should be the target of the alert to automatically start a Vertex AI Pipeline for retraining?

A company has a TensorFlow model trained outside of Google Cloud and wants to use it for online predictions on Vertex AI. They have saved the model in SavedModel format. What is the most efficient way to deploy this model?

An ML team is using Vertex AI to train a deep learning model on a large dataset. To reduce costs, they want to use preemptible VMs for training jobs. However, training must complete within a bounded time. Which strategy should they use?

A team is using Vertex AI Feature Store with an online store for low-latency serving. They notice increasing latency during peak hours. The feature data is updated frequently and requires strong consistency. Which online store type should they use?

Which THREE actions can help improve the performance of a BigQuery ML model?

A logistics company wants to classify shipping documents into categories (invoice, packing slip, bill of lading) using a custom model with minimal code. They have labeled training images. Which Google Cloud service is most appropriate?

A manufacturing company wants to predict equipment failure using sensor data. The data is highly imbalanced (only 1% failures). They are using a gradient boosted tree model with class weights. The model achieves 0.99 recall but 0.2 precision on the test set. Which two actions should they take to improve precision without significantly hurting recall? (Choose TWO)

An ML engineer needs to run batch predictions on tens of petabytes of data using a trained model. The data is stored in Cloud Storage. Which service should they choose?

A company wants to serve a large XGBoost model that exceeds the 2GB limit for Vertex AI Prediction. What should they do?

A team is designing a ML pipeline that includes training, evaluation, and conditional deployment. They want to use Vertex AI Pipelines. Which THREE concepts should they use? (Choose three.)

A company wants to use Vertex AI JumpStart to deploy a pre-trained image classification model and later fine-tune it on their own data. Which TWO statements are true about Vertex AI JumpStart?

A machine learning team uses Vertex AI Pipelines to run a multi-step training pipeline. They want to implement a continuous delivery (CD) process where a model is automatically promoted from staging to production only if it passes an evaluation gate. Which TWO actions should they include in their CI/CD pipeline? (Choose two.)

A team is training a large image classification model using transfer learning from a pre-trained ResNet50. The model will be deployed on mobile devices. They want to fine-tune only the last few layers while keeping the earlier layers frozen. Which approach should they use?

A machine learning team is building a feature engineering pipeline using Dataflow. They need to compute features from streaming data and store them in Vertex AI Feature Store for online serving. The features must be updated within 5 seconds of the event. Which TWO services should they combine? (Select 2)

A data scientist wants to share a trained model with the team for review before deployment. The model is stored in Vertex AI Model Registry. What is the recommended way to grant the team read access to the model?

You have a model that predicts equipment failure. The model is retrained every week with new data. You notice that the model's precision is stable but recall drops suddenly. Which monitoring strategy would best help you understand the cause?

A financial institution needs to extract structured data from scanned PDFs of loan applications, including text fields and tables. They require a human review step for high-risk applications. Which Google Cloud service and configuration should they use?

A team needs to serve a PyTorch model for production inference with strict latency requirements (p99 < 100ms). The model has dynamic control flow and uses custom kernels compiled with torch.jit. Which serving approach should they recommend?

A data science team has trained a custom TensorFlow model for real-time fraud detection. They need to deploy it on Vertex AI with minimal latency and support for multiple concurrent requests. The model requires a GPU for inference. Which machine type should they choose for the Vertex AI endpoint?

A team is using Vertex AI Feature Store to manage features for training and serving. They want to monitor the freshness of the features (i.e., how recently each feature was updated). Which approach should they take?

You have an edge device with limited compute resources. You need to deploy a deep learning model for real-time inference. Which model compression technique should you apply to reduce the model size and latency with minimal accuracy loss?

A data scientist wants to log prediction inputs and outputs for model monitoring. Which Google Cloud service is best suited for this?

A company is migrating from an on-premises ML serving infrastructure to Vertex AI. They have multiple models that need to be served from the same endpoint with different traffic percentages. They also need to monitor prediction quality. Which THREE actions should they take? (Choose 3)

A company is building a document processing pipeline using Document AI to extract data from invoices. They want to ensure high accuracy and handle edge cases where the model may be uncertain. Which THREE steps should they include in their pipeline?

A retailer uses BigQuery ML to build a linear regression model for sales forecasting. The model's evaluation shows high RMSE. Which step should they take first?

An ML team wants to run a hyperparameter tuning job on Vertex AI using a pre-built pipeline component. Which component should they use?

Two teams independently develop two different versions of a model for the same use case. They both deploy to the same Vertex AI endpoint, causing conflicts. What is the best way to manage multiple model versions and avoid conflicts in a collaborative environment?

An organization is deploying a loan approval model and wants to monitor for fairness across demographic subgroups. They have ground truth labels stored in BigQuery. How can they use Vertex AI to evaluate performance disparities between groups?

A company needs to extract entities (e.g., names, dates) from customer emails using a pre-trained model. Which service should they use?

A data scientist needs to train a large PyTorch model on a custom dataset using Vertex AI. The training script expects data from Cloud Storage and uses GPU acceleration. Which option correctly configures a custom training job with a pre-built container for PyTorch and attaches a single NVIDIA V100 GPU?

An engineer deploys a model to a Vertex AI endpoint with minReplicas=1 and maxReplicas=3. The endpoint receives a sudden traffic spike, but it does not scale up beyond 1 replica. The CPU utilization target is 60%. What is the most likely cause?

A team deploys a TensorFlow model using a custom container to Vertex AI Endpoint. The container expects the saved model at the /model directory, but predictions fail with a 'model not found' error. The team used the default Vertex AI serving container in the past. What is the most likely cause?

A retailer wants to implement a recommendation engine that suggests products based on a user's current cart. They have limited ML expertise and want a quick deployment. Which Recommendations AI model type should they use?

An organization wants to implement continuous delivery for their ML model. After a new model is trained and evaluated, they want to automatically deploy it to a staging endpoint, run validation tests, and if passed, promote to production. Which two components should they include in their delivery pipeline? (Choose two.)

A machine learning engineer needs to run batch predictions on 50 TB of data stored in BigQuery using a Vertex AI model. The model is a custom container. What is the most efficient way to set up the batch prediction job?

A data scientist wants to track machine learning experiments, including parameters, metrics, and artifacts, and compare runs. Which Vertex AI service should they use?

An organization is building a continuous training (CT) pipeline that retrains a model whenever one of the following conditions is met: (1) new training data is available in Cloud Storage, (2) it's the first day of the month, or (3) the model's performance degrades below a threshold. Which TWO mechanisms should they combine to trigger the pipeline?

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built Google Cloud Pipeline Components (GCPC) to train a custom model. Which component should the engineer use to submit a custom training job to Vertex AI?

An ML team uses Vertex AI Workbench managed notebooks and wants to version their notebook code and collaborate using Git. Which THREE steps are required to set up Git integration? (Select 3)

A data science team wants to share a set of engineered features across multiple projects and teams to reduce training-serving skew and ensure consistency. They need low-latency serving (single-digit milliseconds) for online predictions and also need to retrieve historical feature values for training. Which approach should they take?

An organization needs to serve a large model (10 GB) with low latency across multiple regions. Which Vertex AI feature best meets this requirement?

A Vertex AI Pipeline contains a task that may produce outputs that are not always needed. The engineer wants to conditionally execute downstream tasks only if a specific artifact is produced. Which KFP SDK v2 construct allows the engineer to implement this conditional execution?

You are an ML engineer at a fintech company. You have a prototype credit risk model built using XGBoost that achieves high accuracy on historical data. The model is trained on a dataset with 500,000 rows and 50 features. The company wants to deploy this model to production to score loan applications in real-time. The production environment must handle a peak load of 100 requests per second with a latency under 200ms. You have decided to use Vertex AI for deployment. After deploying the model as a Vertex AI endpoint with a single n1-standard-4 machine, you notice that latency exceeds 500ms at peak load and some requests time out. You have verified that the model prediction itself (excluding network overhead) takes about 50ms on average. What should you do to meet the latency and throughput requirements?

Which TWO actions should be taken to ensure reproducibility of ML experiments when collaborating across teams on Vertex AI?

You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?

Which THREE of the following are valid ways to share a Vertex AI model across two different Google Cloud projects?

A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?

A small business wants to build a sentiment analysis model for customer reviews without writing any code. They have a small labeled dataset with 500 positive and 500 negative reviews. Which Google Cloud service should they use?

A data scientist is using Vertex AI Workbench user-managed notebooks. They need to collaborate with a colleague on the same notebook. The colleague should be able to edit the notebook simultaneously. What should they do?

A company has a model serving predictions on Vertex AI Endpoints and wants to monitor for prediction drift. They enable Vertex AI Model Monitoring but also need to see a confusion matrix over time. How should they set up the confusion matrix monitoring?

Which TWO actions can help reduce the latency of online prediction requests for a deep learning model served on Vertex AI?

You are designing a distributed training job on Vertex AI for a PyTorch model using DataDistributedParallel (DDP). You have 4 nodes, each with 4 GPUs. What is the total number of workers that should be configured in the TF_CONFIG equivalent for PyTorch?

A data scientist uses Vertex AI Pipelines to orchestrate an ML workflow. They want to reuse a component from Google's curated repository. What is the recommended way to incorporate it?

Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Which Vertex AI service is used to track the lineage of ML pipeline components, artefacts, and executions?

You need to preprocess a large dataset (terabytes) for training a TensorFlow model. The preprocessing includes scaling and bucketizing features, and the same transformations must be applied during serving. Which tool should you use?

A team is using Vertex AI AutoML to train a forecasting model. They need to retrain the model weekly and only if the new week's data significantly changes the data distribution. What is the most efficient way to achieve this?

You are a machine learning engineer at a financial technology company. You have deployed a complex ensemble model consisting of three sub-models (XGBoost, TensorFlow, and PyTorch) for real-time fraud detection. The model is served on Vertex AI online prediction with a custom container that orchestrates the three models sequentially. The endpoint currently uses n1-highmem-8 machines with no accelerators. You are experiencing high latency (avg 500ms) during peak trading hours (9:30 AM - 4:00 PM EST), exceeding the 200ms SLA. The container is CPU-bound, and memory usage is around 60%. The model weights total 500 MB. You have already tried increasing the batch size per request from 1 to 4, which reduced latency slightly but not enough. The traffic pattern is very spiky, with sudden bursts of up to 1000 requests per second. Your goal is to meet the latency SLA without significantly increasing cost. Which action should you take?

A company implements an ML pipeline using Vertex AI Pipelines. The pipeline trains a model using custom training jobs and then deploys it to an endpoint. The team notices that the endpoint occasionally serves an older model version for a few minutes after a new pipeline run completes. What is the most likely cause?

A company runs a Vertex AI pipeline that uses a container component to preprocess data. The component downloads a large file from a public URL and saves the output to Cloud Storage. The pipeline fails intermittently with a 'timeout' error. Which THREE steps should the team take to improve reliability? (Choose three.)

You are designing a batch prediction pipeline using Vertex AI. The input data is 100 TB of images stored in Cloud Storage. The model is a custom TensorFlow model that expects TFRecord format. The pipeline must be cost-effective and run within a time window of 2 hours. Which THREE steps should you include?

You have an online prediction model that is showing increasing prediction latency. You have already verified that the request rate and input data size are unchanged. Which of the following should you investigate next?

A company wants to transcribe customer service calls in real-time. The audio is telephony quality (8 kHz). Which Speech-to-Text model should they specify?

A machine learning engineer needs to deploy a model to an endpoint for real-time predictions. The model is registered in Vertex AI Model Registry. Which command should they use to create an endpoint and deploy the model with the alias 'champion'?

A company needs to analyze customer feedback from app reviews to identify common themes and sentiment. They have millions of reviews in multiple languages. Which combination of pre-built APIs should they use?

A data science team needs to serve multiple versions of the same ML model on Vertex AI Endpoints for A/B testing. They want to gradually shift traffic from the current 'champion' model to a new 'challenger' model. Which feature should they use?

A company uses Vertex AI Prediction with a custom container for a TensorFlow model. They notice that after deploying a new model version, requests still go to the old version. What is the most likely cause?

A retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)

A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

You are monitoring a machine learning pipeline that runs on Vertex AI Pipelines. The pipeline occasionally fails with a 'ResourceExhausted' error when attempting to read data from BigQuery. Which action should you take to resolve this issue?

A company has a prototype ML model that achieves 85% accuracy on historical data. In production, accuracy drops to 70% after two weeks due to data drift. They need an automated retraining pipeline with minimal manual oversight. Which solution is most cost-effective?

An ML engineer is building a monitoring dashboard for a Vertex AI pipeline that includes training, evaluation, and batch prediction. Which THREE components should be included to provide comprehensive observability? (Select THREE.)

A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

You have a very large language model that does not fit on a single GPU. You need to train it efficiently across multiple GPUs on a single machine. Which approach should you use?

Refer to the exhibit. A team runs this command to upload a model to Vertex AI. They want to create this model as a new version under an existing model named 'my_model'. What is missing from the command?

What is the primary purpose of Vertex AI Edge Manager?

To enable collaboration on notebook-based experiments across teams, what is the recommended approach in Google Cloud?

A retail company deployed a demand forecasting model using TensorFlow on Vertex AI Batch Prediction. The model runs weekly on a large dataset stored in BigQuery. Over the past month, the prediction accuracy has degraded significantly. The ML engineer reviews the monitoring dashboard and sees that the feature distribution for 'product_price' has shifted from a mean of $50 to $55, and the new product category 'electronics' now represents 20% of the data, whereas it was only 5% in training. The model was never retrained after initial deployment six months ago. The engineer also notices that the Vertex Explainable AI feature importance scores have changed: 'product_price' used to be the top feature (importance 0.35) but now ranks third (importance 0.20). The company requires minimal downtime and wants to improve accuracy as quickly as possible without incurring high costs from excessive retraining. Which course of action should the ML engineer take?

A large organization uses a multi-project setup with a central data lake. Different teams manage their own models. To enable cross-team sharing of features, they want to use Vertex AI Feature Store. What is the best practice to manage access?

A machine learning team uses Vertex AI Pipelines to orchestrate their training pipeline. They want to trigger the pipeline automatically in response to new data arriving in a Cloud Storage bucket, and also support a scheduled run every day at 6 AM. Which combination of services should they use to achieve both event-driven and schedule-based triggers?

Which Vertex AI feature allows you to reduce the size of a trained model to improve inference speed on edge devices without significant accuracy loss?

Which THREE of the following are valid best practices when using Vertex AI AutoML for tabular data?

A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

A machine learning team wants to implement a continuous delivery pipeline for their ML models using Vertex AI Pipelines. The pipeline should automatically deploy a model to a staging endpoint after evaluation passes, and then after manual approval, promote it to production. Which strategy should they use to manage model versions in the Vertex AI Model Registry?

A logistics company uses a regression model to predict delivery times. The model currently uses features: distance (km), traffic index, weather condition, and time of day. The data scientist notices that the model's predictions are systematically too low for deliveries during peak traffic hours. Which action would best address this issue?

A company needs to detect objects in real-time from a live video feed. They want to use a pre-trained model with minimal setup. Which Google Cloud service should they use?

You are deploying a model on Vertex AI and need to ensure high availability and low latency. Which THREE configurations should you implement?

A machine learning engineer notices that the online prediction latency for a custom TensorFlow model deployed on Vertex AI has increased significantly over the past week. Cloud Monitoring shows that the CPU utilization of the endpoints remains below 40%, but the number of concurrent requests has doubled. What is the most likely cause of the latency increase?

A data scientist needs to share a BigQuery dataset with a colleague in a different team so they can run queries. What is the simplest and most secure way to grant access?

You have a TensorFlow training script that runs on a single machine. To speed up training on Vertex AI with 8 GPUs on a single machine, which strategy should you use?

Your team is using Vertex AI Pipelines to build an automated training pipeline. You need to share the pipeline definition with another team so they can run it in their own project. Which format should you use?

An organisation wants to monitor fairness of their loan approval model across demographic subgroups. They have predictions stored in BigQuery along with ground truth. Which GCP service can evaluate model performance for each subgroup and identify disparities?

What is the purpose of the 'importer' component in Vertex AI Pipelines?

A company wants to classify customer support emails into categories like 'billing', 'technical', or 'account'. They have labeled email text data. Which AutoML solution should they use?

In a Vertex AI Pipeline, a component produces a Metrics artifact that includes an evaluation metric. The engineer wants to use this metric value as a condition to decide whether to deploy the model. However, the metric value is stored in the artifact's metadata and not directly as a pipeline parameter. How can the engineer pass the metric value to a downstream conditional task?

You want to deploy a TensorFlow model to a Vertex AI endpoint and enable online predictions. The model requires GPU for inference. Which machine type should you select when deploying the model?

You are performing hyperparameter tuning on Vertex AI with Vizier. You want to maximize the accuracy of your model, and you have a budget of 50 trials. Which algorithm should you choose to best explore the search space?

An ML engineer is monitoring a Vertex AI Feature Store used for online serving. Which metrics are most important to track for ensuring low-latency online serving?

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

An ML engineer is monitoring a model on Vertex AI Endpoint and sees that feature 'age' has a training distribution of (mean=45, std=10) but the serving distribution over the last hour shows (mean=30, std=15). JS divergence is 0.12, but the alert threshold is 0.1. The engineer suspects this is due to a temporary campaign targeting younger users. What should they do first?

A team is training a custom TensorFlow model on Vertex AI using a pre-built container. They need to use a TPU pod slice (v3-32). What THREE actions are required to set up the training job correctly?

An organization is deploying a mission-critical model on Vertex AI Endpoints. They need to ensure high availability and meet a strict SLO of 99.9% uptime. Which THREE steps should they take? (Choose 3)

An organization uses Cloud Composer to orchestrate ML workflows. A DAG that triggers Vertex AI training jobs fails because the training job exceeds the 7-day maximum runtime. What is the best way to handle long-running training jobs in Cloud Composer?

Which of the following is a best practice when designing idempotent pipeline components in Vertex AI?

A company is using Vertex AI Pipelines to automate model retraining. They have a component that creates a BigQuery table with training data. To ensure idempotency, the component should check if the table already exists and recreate it if necessary. What is the best practice for passing data between pipeline components?

A data scientist has trained a model using Vertex AI Training and wants to deploy it to a Vertex AI Endpoint for online predictions. Which orchestration service should be used to automate the deployment step after training completes?

You are using Cloud Datalab for collaborative data exploration with your team. However, some team members cannot access the Datalab instances. What is the most likely issue?

A team has deployed a model with autoscaling configured as shown. They notice that during off-peak hours, the endpoint consistently runs 3 instances instead of scaling down to 1. What is the most likely cause?

A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?

An organization wants to implement continuous training for a model that serves predictions via Vertex AI Endpoints. Which approach best automates the retrain-deploy cycle?

A team is architecting a low-code ML system for real-time predictions with AutoML. Which THREE considerations are critical for production?

Which TWO options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)

A user receives the error "Deployment failed due to insufficient memory. Please use a machine type with higher memory." when deploying an AutoML model. What should they do?

A team is using Vertex AI Model Monitoring and wants to set up automated retraining when drift is detected. Which THREE services are needed to implement this pipeline? (Choose three.)

A company has a pipeline that uses Vertex AI Pipelines to fetch data from BigQuery, preprocess with Dataflow (without code?), then train an AutoML model, and deploy. However, they want to reduce cloud costs. The pipeline runs hourly. Which change will most reduce compute costs while maintaining throughput?

An organization uses Vertex AI Pipelines to automate a model training workflow. They want to reuse previously trained models if the data hasn't changed. Which pipeline component best achieves this?

A data scientist is defining a Vertex AI pipeline and needs to include a step that imports a pre-existing model from Cloud Storage into the pipeline as an artifact. Which Kubeflow Pipelines SDK v2 component should they use?

A data science team uses TFX to train and deploy a model on Vertex AI. They want automated monitoring for pipeline health. Which set of metrics should they monitor to quickly detect issues in the training pipeline?

You are fine-tuning a large language model (LLM) from Hugging Face Transformers using Vertex AI Training. The model has 7 billion parameters and does not fit into the memory of a single GPU. You need to train across multiple GPUs, splitting the model layers across devices. Which distributed training approach should you use?

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

A company runs batch predictions on a large dataset using Vertex AI Batch Prediction. They want to reduce costs without significantly increasing processing time. Which three actions should they take? (Choose three.)

Which TWO metrics should you monitor to detect data drift in a batch prediction pipeline?

A machine learning team is deploying a PyTorch model on Vertex AI Prediction for real-time inference. The model was trained with preprocessing that includes tokenization and normalization. They want to embed the preprocessing logic in the model to reduce prediction latency and avoid additional service calls. Which approach should they take?

A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?

You need to deploy a model for online predictions with low latency. You want to ensure that the endpoint can handle traffic bursts without cold start. Which TWO configurations should you set? (Choose 2)

You are fine-tuning a pre-trained model using transfer learning. The new dataset is small and very similar to the original training data. To avoid overfitting, which layer freezing strategy should you adopt?

An ML engineer is using Vertex AI distributed training for a TensorFlow model that uses the MirroredStrategy. They notice that the training throughput drops significantly when moving from a single GPU to multiple GPUs on the same machine. What is the most likely cause?

You are using DVC for data versioning in an ML project on Google Cloud. Your training data is stored in Cloud Storage. You want to track a new version of the dataset after preprocessing. Which DVC command should you use to register the changes?

An ML team is optimizing an inference model for deployment on edge devices. They need to reduce the model size and improve latency while maintaining accuracy as much as possible. Which two techniques should they use? (Choose TWO.)

An ML team is scaling a prototype to production. The data pipeline currently reads from Cloud Storage and transforms data with a custom Python script. They need to handle higher throughput and add monitoring. Which approach should they take?

A company has a Vertex AI pipeline that trains a model on streaming data from Pub/Sub. The pipeline is triggered by a Cloud Function when new data arrives. Recently, jobs have been failing with 'ResourceExhausted: Quota limit exceeded for regional CPUs in us-central1.' The team needs to ensure successful job execution while minimizing changes. Which approach should they take?

A company is using Vertex AI Prediction with a custom container that performs preprocessing before inference. The preprocessing step is CPU-intensive and the inference step uses a GPU. They want to minimize prediction latency while optimizing cost. Which architecture should they use?

You are a machine learning engineer at a retail company. You have deployed a product recommendation model on Vertex AI Prediction using a custom container. The model is a TensorFlow SavedModel that computes embeddings using a large lookup table. The endpoint is configured with 2 replicas on n1-standard-4 (4 vCPU, 15 GB memory) machines. After deployment, you notice that the endpoint's memory usage grows over time, eventually reaching 90% and causing requests to fail with 503 errors. The container logs show no errors, but the memory usage graph shows a steady increase. The model loads the embedding table (5 GB) at startup. You suspect a memory leak. Which course of action should you take first to diagnose and resolve the issue?

A developer wants to quickly deploy a pre-trained foundation model for text generation without writing any code. Which Vertex AI feature should they use?

Your team is deploying a large language model (LLM) on Vertex AI for online prediction. The model exceeds the maximum request size for Vertex AI Prediction. Which approach should you take to serve this model?

A data science team uses Cloud Composer to orchestrate a complex ML workflow. They need to run a Vertex AI pipeline and then a BigQuery query conditionally based on the pipeline's output. Which Airflow features should they use? (Choose two.)

Refer to the exhibit. A data scientist deploys a new model version (model_v2) to an existing endpoint with 20% traffic. After a few days, they notice that model_v2's error rate is higher than model_v1's. They want to route all traffic back to model_v1 immediately. Which command achieves this with minimal disruption?

You have deployed a regression model that predicts house prices. Over the past month, the model's predictions have been consistently too high. You suspect data drift in the input features. Which monitoring metric should you prioritize to confirm this?

Match each Google Cloud AI/ML service to its primary purpose.

A retail company wants to build a product recommendation system using customer purchase history and product attributes. They have limited ML expertise and want to minimize custom code. Which approach should they choose?

A company uses Cloud Scheduler to trigger Cloud Functions that submit Vertex AI training jobs. They want to ensure fault tolerance and minimize manual intervention. Which TWO practices should they implement?

A data science team uses Vertex AI Experiments to compare multiple model training runs. They want to capture and compare hyperparameters, metrics, and code versions for each run. Which TWO steps should they take?

A team has trained a sentiment analysis model using PyTorch on Vertex AI Training. They now want to deploy it for online predictions with low latency. Which TWO actions should they take? (Choose 2)

A large e-commerce company deploys a recommendation model on Vertex AI with autoscaling enabled. During Black Friday, traffic spikes rapidly. The autoscaler adds new instances, but new instances take several minutes to become ready (cold start). As a result, many requests time out. What should they do to mitigate this issue?

A data scientist needs to scale a prototype deep learning model to train on a massive dataset using multiple GPUs. Which three strategies are essential for efficient distributed training? (Select THREE)

An MLOps team wants to automate the retraining of a model each time new data arrives in a BigQuery table. What is the most efficient Google Cloud service to orchestrate this pipeline?

A machine learning engineer has a Vertex AI pipeline that trains a model. The pipeline uses caching to avoid re-running components that have not changed. After updating the training code, the engineer notices that the pipeline still uses cached outputs from the previous run. What could be the reason?

A machine learning engineer wants to use Vertex AI Vizier to tune three hyperparameters: learning rate (log scale), number of layers (integer), and optimizer (categorical). They have 50 parallel trials available. Which parameter specification types should they define?

An ML engineer needs to deploy a model to an endpoint and gradually shift traffic from the previous version (champion) to a new version (challenger) for A/B testing. How should they configure the endpoint?

A company is deploying a machine learning model for real-time inference on Vertex AI. Which TWO practices improve serving performance and reliability?

Refer to the exhibit. A ML engineer runs this Vertex AI pipeline. After execution, the "train" task fails with a resource exhaustion error. The task consumes more memory than allocated. Which step should the engineer take to fix this issue without increasing the overall quota cost?

Which TWO are best practices for deploying models to Vertex AI Prediction? (Choose 2.)

An ML team is using Population Stability Index (PSI) to monitor feature drift on a Vertex AI Endpoint. The PSI value for a feature is 0.25, which exceeds the alert threshold of 0.2. The feature has high SHAP importance. The team wants to automatically retrain the model. What is the correct end-to-end setup?

A company uses Vertex AI Feature Store for feature engineering. They need to ensure point-in-time correctness to avoid data leakage during training. Which feature retrieval method should they use?

A company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?

An ML engineer is building a continuous training pipeline that retrains a model when new data arrives. The pipeline should also detect skew between training and serving data. Which TWO Google Cloud services should they use? (Choose two.)

A data scientist creates a custom Python function component for a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. The component takes a string parameter 'input_text' and outputs a Metrics artifact. The scientist wants to include a lightweight Python function without building a container. Which code snippet correctly defines this component?

A hospital wants to deploy a machine learning model for detecting anomalies in patient vital signs. The model was trained on historical data but must comply with HIPAA regulations. The model serving must be low-latency (under 100 ms) and handle up to 1000 requests per second. Which architecture should they use on Google Cloud?

You are fine-tuning a Gemma model using Vertex AI JumpStart. You want to combine the fine-tuned model with a custom output layer for a unique task. Which TWO components are required to deploy the combined model? (Choose 2)

You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recommendation that runs on Vertex AI Prediction. The model is deployed on a single n1-highmem-2 instance (CPU only) with autoscaling enabled (min replicas=1, max replicas=10). During Black Friday, traffic spikes to 1000 requests per second (QPS), and you observe that latency increases from 50ms to over 5000ms, and many requests time out. You check the monitoring dashboard and see that CPU utilization is at 100% on the single instance, and autoscaling is not triggering quickly enough. The team has a budget for this service and wants to handle the spike without compromising latency. What should you do?

A startup has developed a prototype ML model using scikit-learn on a single machine. They now need to scale it to handle larger datasets and deploy it for real-time predictions. The team is small and wants minimal operational overhead. Which Google Cloud service should they use?

An ML team wants to monitor their recommendation model for fairness. Which TWO metrics should they track to detect potential bias? (Select TWO.)

You deploy a PyTorch model to Vertex AI Online Prediction. After deployment, you observe that inference latency is approximately 300ms per request, but the desired SLA is under 100ms. The model uses a custom container with CPU only. Which action is most likely to reduce latency to the target?

A team is monitoring a deployed model and notices that the prediction distribution has changed significantly over the last week. They want to detect which features are contributing most to the drift. Which tool should they use?

A company deploys a classification model on Vertex AI for loan approval. After a month, they notice the precision has dropped significantly. What should they do first?

A data science team uses Cloud Composer to orchestrate ML workflows. They need to trigger a Vertex AI pipeline after a BigQuery data load completes, and then run a Dataflow job. Which Airflow operator should they use to launch the Vertex AI pipeline?

A company needs to serve a model with strict latency requirements (<100ms). They are using Vertex AI Prediction with CPU. During testing, latency is 150ms. What should they do?

A company is implementing MLOps on Google Cloud and needs to manage model versions, assign aliases (e.g., 'champion' for production, 'challenger' for staging), store evaluation metrics alongside each model version, and deploy models to endpoints. Which service should they use? (Choose THREE that are part of the solution.)

A data scientist needs to forecast daily sales for the next 30 days using historical sales data stored in BigQuery. They want to use BigQuery ML. Which model type should they choose?

Your team is using Vertex AI Prediction for a large-scale NLP model (PyTorch, custom ops). The model currently runs on CPU but you want to optimise inference cost and performance. Which THREE approaches should you consider? (Choose 3)

A team is monitoring a model and observes that the error rate (prediction failures) has increased. They have enabled request/response logging on the Vertex AI Endpoint. How can they set up a metric and alert for prediction error rate?

A machine learning team wants to implement champion/challenger model deployment. They have two model versions: v1 (champion) and v2 (challenger). They deploy both to the same endpoint with traffic splitting. How should they manage model versions in Vertex AI Model Registry to reflect this?

A company wants to monitor the cost of their Vertex AI prediction endpoint. They are charged per hour per replica and per request for GPU instances. Which approach should they use to track these costs?

A company uses Vertex AI Pipelines to train and deploy models. The pipeline has a step that runs a custom container. The step fails intermittently with a timeout error. Which approach should be taken to robustly handle this?

A company wants to predict customer churn using a dataset with 10,000 rows and 20 features. They have no ML expertise. Which low-code solution should they use?

A team is using Vertex AI Experiments to compare different hyperparameters. They want to automatically record the hyperparameters. What is the correct way?

A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?

An organization wants to deploy a model on edge devices (e.g., Android phones) for offline inference. They trained a model using TensorFlow. Which THREE steps should they take to prepare and deploy the model?

A data scientist wants to use a pre-trained ResNet model from Keras Applications and fine-tune it on a small custom dataset. Which approach should they take to avoid overfitting?

Which THREE should be considered when setting up an automated retraining pipeline using Vertex AI Pipelines and Cloud Composer? (Choose THREE.)

You deploy a new version of a model to a Vertex AI endpoint and want to gradually shift traffic from the old version to the new version over 24 hours. The endpoint currently serves 100% traffic to the old version. What should you do?

A company deploys a model on Vertex AI Endpoints for real-time inference. They notice latency spikes during peak hours. Which action is most effective to reduce latency without sacrificing accuracy?

A company uses Vertex AI Vector Search for similarity search. They have a dataset of 10 million 512-dimensional vectors. Which index type should they choose for lowest latency at high recall?

An engineer is using TensorFlow Transform (tf.Transform) to preprocess training data. They want to ensure that the same preprocessing logic is applied during inference without code duplication. Which approach should they take?

You need to orchestrate a complex ML workflow that involves multiple Vertex AI pipelines, BigQuery jobs, and Dataflow pipelines. The workflow must handle dependencies, retries, and monitoring. Which two services are best suited for this orchestration?

A machine learning engineer needs to pass a large dataset between two components in a Vertex AI pipeline. What is the recommended way to pass this data?

An ML engineer needs to update a model deployed on a Vertex AI endpoint without downtime. They want to gradually shift traffic to the new version while monitoring for errors. What is the correct procedure?

You need to deploy a TensorFlow model to edge devices for real-time inference with minimal latency. The model is currently trained on Vertex AI. Which approach should you use?

A team uses Vertex AI Experiments to track ML training runs. They want to automatically trigger a retraining pipeline when new labeled data arrives in BigQuery, and ensure the pipeline uses only approved libraries from a central artifact registry. Which combination of services should they use?

A team wants to implement CI/CD for their ML models using Cloud Build. They have a pipeline that trains a model and deploys it. What is the best practice for triggering the pipeline when a new commit is pushed to the source repository?

A company uses a Cloud Composer DAG to run a daily ML pipeline that includes Dataflow jobs and model training on Vertex AI. The pipeline frequently fails due to insufficient permissions when the Dataflow worker accesses data in Cloud Storage. What is the most efficient way to resolve this issue?

You need to serve a large embedding model for similarity search with low latency. The model was trained to generate 256-dimensional embeddings. You plan to use Vertex AI Vector Search. Which index type should you choose to balance accuracy and performance for a dataset with 10 million vectors?

What is the most likely cause of the error?

Which TWO of the following are benefits of using Vertex AI Matching Engine (Vector Search) over a brute-force nearest neighbor search? (Choose 2)

Your organization has a large production system that uses Vertex AI Prediction for an NLP model with a 2 GB memory footprint. The endpoint is configured with 5 replicas, each using an n1-standard-4 with a single T4 GPU. Recently, you observed an increase in 503 errors during peak hours. Cloud Monitoring shows that GPU utilization is consistently above 90% across all replicas, while CPU and memory are below 50%. You have already increased the max replicas to 10, but the errors persist because the increased replicas also become saturated. What should you do to resolve the issue?

You are deploying a scikit-learn model for online predictions. The model size is 200 MB. You want to minimize latency and cost. Which serving option should you choose?

A team uses Vertex AI Feature Store for storing features. They want to share feature definitions with other teams in a collaborative manner. What is the best way to collaborate on feature definitions?

A team deploys a PyTorch model on Vertex AI for online predictions. They notice that after deployment, the latency increases over time, especially during peak hours. The model is served using a custom container. What is the most likely cause?

A company uses BigQuery as their data warehouse. They want to version datasets for ML experiments and be able to query snapshots at specific points in time. Which approach is most cost-effective and requires minimal operational overhead?

A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

A company has deployed a model for image classification and wants to monitor for feature drift using XRAI attributions. However, they notice that the XRAI attribution maps are too large and are causing high latency in the monitoring pipeline. What is the most effective way to reduce the overhead of explainability monitoring for image models?

A team uses Vertex AI Feature Store with an online store for real-time predictions. They notice that the online store queries are taking longer than expected. Which TWO actions could improve online store performance? (Choose 2)

An organization wants to use Cloud Composer (Airflow) to orchestrate a machine learning workflow that includes running a Vertex AI Pipeline, followed by a BigQuery job, and then a Dataflow pipeline. What is the primary advantage of using Cloud Composer for this orchestration?

A financial institution wants to use Natural Language API for sentiment analysis on customer feedback, but the domain-specific language (e.g., 'bullish', 'bearish') is not correctly classified. They have 200 labeled examples. Which approach minimizes coding effort while improving accuracy?

A machine learning engineer is deploying a TensorFlow model on an edge device with limited memory and compute. The model needs to perform inference with low latency. The engineer has a trained float32 model. Which model compression technique should be applied first to reduce the model size and improve inference speed without significant accuracy loss?

An organization uses Vertex AI Workbench user-managed notebooks and wants to enable collaboration where multiple data scientists can edit the same notebook simultaneously. Which configuration should they use?

A team wants to share a trained model with another team who will deploy it to a different Google Cloud project. Which is the recommended way to transfer the model?

You are A/B testing a new model version (challenger) against the current version (champion) on Vertex AI. You want to gradually shift traffic from champion to challenger while measuring business metrics. Which approach should you use?

A data scientist notices that the prediction distribution of a deployed model has changed significantly over the past week. They want to identify which features are contributing most to the drift. Which approach should they use?

Your team has deployed a model on Vertex AI endpoints and you are planning an A/B test to compare a new challenger model (v2) against the current champion (v1). The test should measure business metrics such as click-through rate. Which THREE steps should you take to set up the A/B test correctly? (Choose 3 correct answers)

A machine learning engineer is scaling a prototype natural language processing model that uses a transformer encoder. The prototype was trained on a small corpus on a single GPU. For production, they need to train on a much larger corpus using TPUs on Vertex AI. They convert the TensorFlow code to work with TPUStrategy. The training starts but after a few steps, the loss becomes NaN and training diverges. The learning rate scheduler uses a warm-up and then linear decay. The initial learning rate is 1e-4. The batch size per TPU core is 32, with 8 cores total (batch size 256). What is the most likely cause?

You need to create a reproducible snapshot of a BigQuery table as of a specific timestamp for ML model training. The snapshot should be queryable without copying the entire dataset. Which BigQuery feature should you use?

A data science team deploys a regression model to predict house prices. After one month, the mean absolute error (MAE) on the serving data increases by 20% compared to the test set. Which monitoring strategy should the team implement first to diagnose the issue?

You are setting up feature monitoring in Vertex AI Feature Store to detect drift in a numerical feature. The monitoring job should run daily and alert if the Jensen-Shannon divergence exceeds 0.1. Which configuration should you use?

An ML team uses Vertex AI Pipelines to automate model retraining. The pipeline includes a step that queries BigQuery to create a training dataset. The team notices that the pipeline fails intermittently with a '403 Exceeded rate limits' error. What is the most likely cause and solution?

A data scientist uses Vertex AI Workbench to train a model and then deploys it to an endpoint. They want to automate the retraining and redeployment pipeline when new data arrives. Which service should they use?

A company needs to run batch predictions on 10 TB of data stored in Cloud Storage. The predictions should be written to BigQuery. Which approach should they use?

A company uses Vertex AI Pipelines for ML training. They want to implement continuous training triggered by new data arrival. Which two Google Cloud services should they use to achieve this? (Choose two.)

A media company wants to build a real-time recommendation system for articles. They have a large user base (10M+) and frequent updates to user interactions. They need to handle cold-start users and new articles. Which architecture on Vertex AI is most suitable?

A team uses Vertex AI Feature Store for online serving. They notice high latency during peak hours. They have configured the feature store with Bigtable as the online serving store. What is the most likely cause of the high latency?

What does the `ML.PREDICT` command do in BigQuery ML?

Which TWO tools can be used to collaborate on feature definitions across teams?

Which TWO of the following are recommended methods to ensure data privacy when collaborating with external partners on ML projects?

You need to deploy a PyTorch model for online inference on Vertex AI but the model was trained using custom ops that are not natively supported. You want to use NVIDIA Triton Inference Server for optimisation. How should you proceed?

A data engineer wants to orchestrate a complex workflow that includes running a Vertex AI pipeline, then a BigQuery job, and finally a Dataflow pipeline. The workflow must handle dependencies, retries, and monitoring. Which Google Cloud service is most suitable for this orchestration?

An ML engineer needs to run batch predictions on 10 TB of data stored in BigQuery using a TensorFlow model. The predictions must be written to BigQuery. Which service should they use?

A data science team uses Vertex AI Pipelines to build a training pipeline. They notice that when the pipeline fails due to a transient error in a component, the entire pipeline restarts from the beginning, taking a long time. What is the best practice to handle transient errors efficiently?

A company deploys a batch prediction job on Vertex AI using a custom container. The job completes successfully, but the predictions are later found to be inaccurate. The ML engineer wants to set up monitoring to detect similar issues proactively. Which approach should the engineer take?

A team uses Vertex AI Workbench managed notebooks. They want to version control their notebook files and collaborate using Git. What is the best way to integrate Git?

A company has a TensorFlow model for image classification that must run on edge devices with limited memory. They need to reduce the model size without significant accuracy loss. Which technique should they use?

You want to use Vertex AI JumpStart to quickly deploy a pre-built foundation model for text summarization. Which action is required?

You are troubleshooting a Vertex AI endpoint for a customer. The exhibit shows the endpoint configuration. The customer reports that Model A is experiencing high latency during peaks. Model B runs fine. What is the most likely cause?

A financial services company uses Vertex AI to build credit risk models. They have a team of 10 data scientists and 3 ML engineers. They use multiple notebooks in Vertex AI Workbench, storing data in Cloud Storage and BigQuery. The team reports that training jobs sometimes fail with 'Permission denied' errors when reading from certain Cloud Storage buckets. The error occurs intermittently and only for some users. The team uses custom service accounts for each user's notebook instance, but the permissions seem inconsistent. The IT security team has enforced that all service accounts must have least privilege. What is the most effective course of action to resolve the permission issues while maintaining security?

A company needs to perform sentiment analysis on streaming social media data. Which architecture should they use?

When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?

A team uses Cloud Build to automatically trigger a Vertex AI pipeline when changes are pushed to the model code repository. They have a cloudbuild.yaml file that builds a container image and submits the pipeline. However, they want to run the pipeline only if the commit includes changes to the 'training/' directory. Which Cloud Build configuration option should be used to filter the trigger?

A financial services company uses Vertex AI to deploy multiple models for fraud detection. The ML team has set up a CI/CD pipeline using Cloud Build and Cloud Deploy. The pipeline builds a custom container with the trained model, pushes it to Artifact Registry, and deploys it to a Vertex AI Endpoint. Recently, a new regulation requires that all model deployments be audited and approved by the compliance team before going live. The compliance team wants to review the model's evaluation metrics and approve the deployment via a ticketing system. Currently, the CI/CD pipeline automatically deploys after the container is built. The team needs to implement a gating process without slowing down the development cycle. What should they do?

A data engineer wants to use BigQuery ML to train a model for predicting customer churn (binary classification) using a large dataset. They want the model to be automatically tuned. Which model type should they choose?

A financial institution needs to deploy a fraud detection model with strict latency <100ms per prediction and high throughput (1000 predictions/sec). The model is a deep neural network. Which architecture on Google Cloud meets these requirements?

A data analyst wants to train a binary classification model on a BigQuery table without moving data out of BigQuery. They have limited ML expertise. Which approach should they take?

Which TWO of the following are low-code machine learning solutions on Google Cloud?

Which TWO actions are recommended for collaborating on machine learning models using Vertex AI Model Registry?

A company wants to implement continuous delivery (CD) for ML models, where a model is automatically deployed to a staging environment and only promoted to production after passing an evaluation gate. Which combination of GCP services is BEST suited for orchestrating this CD pipeline?

A company wants to classify support ticket text into categories. They have labeled historical tickets. Which Google Cloud service allows them to train a custom classification model with no code?

Refer to the exhibit. An alert policy is configured to trigger when prediction latency exceeds 500 ms for 5 consecutive minutes. The team is experiencing many false positive alerts during brief latency spikes. Which adjustment would most effectively reduce false positives while still detecting prolonged latency issues?

A company needs to serve a model for real-time predictions with a strict latency SLA of 100ms at the 99th percentile. The model is lightweight and traffic patterns are highly variable with occasional spikes. Which deployment strategy best meets the SLA while controlling cost?

Your team is using Vertex AI Feature Store for online predictions. You notice that feature values for some entities are missing in production, leading to failed predictions. Upon investigation, you find that the ingestion pipeline has been failing intermittently. What is the best immediate course of action to prevent prediction failures?

A company wants to use BigQuery ML to train a DNN_CLASSIFIER model on a dataset with 100 million rows. They are concerned about training time and cost. Which approach can help optimize training performance while staying within BigQuery ML?

A team runs a Vertex AI pipeline daily. They notice that a component that downloads a file from a public URL always executes even when the URL and parameters haven't changed. They want to avoid unnecessary re-execution and reduce costs. What should they do?

A company uses Vertex AI for AutoML training. Which THREE are best practices for managing model versions?

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

A machine learning engineer is monitoring a deployed churn prediction model that has shown a gradual decline in accuracy over the past month. The engineer wants to diagnose the root cause of the performance degradation. Which TWO actions should the engineer take? (Choose two.)

A data science team uses Vertex AI Pipelines to orchestrate ML training. They notice that some pipeline runs are failing because of inconsistent data schemas. They want to enforce schema validation as a gate before the training step executes. Which approach should they implement?

A data scientist wants to create a Vertex AI pipeline component that uses a custom container image stored in Artifact Registry. The component should accept a dataset artifact as input and output a model artifact. Which component type should they use?

An ML engineer needs to deploy a model to Vertex AI for online predictions and enable autoscaling to zero when not in use. Which THREE conditions must be met? (Choose 3)

You need to perform a large-scale feature computation on streaming data from Pub/Sub, transforming raw events into features, and writing results to Vertex AI Feature Store for online serving. Which Google Cloud architecture is most appropriate?

A company needs to serve a high-throughput prediction service with strict latency requirements. They want to minimize cold starts and ensure consistent performance. Which endpoint configuration is most appropriate?

You manage a multi-tenant serving system on Vertex AI Prediction where multiple models are deployed in a single endpoint using model versioning. One particular model version (v2) is consuming excessive resources, causing latency spikes for other versions. You need to isolate this model to prevent interference. The models are all in TensorFlow SavedModel format. What is the best approach?

You are deploying a new version of a model to a Vertex AI endpoint that already has a champion model serving 100% of traffic. You want to gradually shift traffic to the new version while monitoring for errors. Which approach should you use?

A company needs to maintain an audit trail of model changes for compliance. Multiple teams will be updating models. What is the best approach to track who created, modified, or deployed each model version?

An application serving predictions from a Vertex AI endpoint receives many identical requests within a short time window. The team notices redundant computation and wants to cache responses to reduce latency and cost. What is the recommended solution?

A company needs to forecast product demand for the next 12 months using historical sales data. They want to use BigQuery ML with minimal coding. Which model type is most suitable?

A company deploys a custom ML model on Vertex AI to predict customer churn. The model retrains weekly, and predictions are served via a Vertex AI endpoint. After a recent retraining, the monitoring dashboard shows a sudden increase in prediction requests but a decrease in predicted churn probabilities. The model's accuracy on the validation set remains stable. What is the most likely cause of the observed behavior?

A team wants to deploy two versions of a model (v1 and v2) on Vertex AI Endpoint to conduct an A/B test. They need to split traffic so that 10% of requests go to v2. Which configuration achieves this?

A model deployed on Vertex AI Endpoints shows increasing prediction latency. What is the most scalable way to reduce latency?

Which THREE practices improve collaboration when using Cloud Composer for ML pipelines?

A recommendation system model is updated daily via a retraining pipeline. After each update, the online prediction latency increases significantly for about 30 minutes before returning to normal. What is the most likely cause and solution?

You are deploying a deep learning model on edge devices with limited computational resources. The model must run inference in <10 ms and the model size must be under 50 MB. Currently, your trained model is 200 MB and runs in 50 ms. Which combination of model compression techniques should you apply?

A team uses Vertex AI Pipelines to automate training and deployment. They need to ensure that only models that pass a set of quality checks (e.g., accuracy > 0.9, latency < 100ms) are deployed to production. How should they implement this?

You are an ML engineer at a logistics company. The company uses a Vertex AI Pipeline with BigQuery ML to train a model that predicts delivery delays based on weather, traffic, and historical order data. The pipeline runs daily and includes steps: (1) data extraction from BigQuery, (2) feature engineering using Dataflow, (3) model training with BigQuery ML (logistic regression), (4) model evaluation, and (5) conditional deployment to a Vertex AI Endpoint if accuracy > 0.85. Recently, the pipeline has been failing at step 5 with the error: "Vertex AI Endpoint creation failed: Quota limit of 1 endpoint per region exceeded." The company has already created one endpoint in the same region for another model. The pipeline is configured to create a new endpoint each time a model is deployed. The engineer needs to fix this with minimal changes to the pipeline code. Which course of action should the engineer take?

An organization wants to trigger a Vertex AI pipeline whenever new data arrives in a Cloud Storage bucket. Which approach should they use?

A developer wants to add text translation to a mobile app. They need to translate user-generated content into multiple languages, and latency is critical. Which pre-built API should they use?

A team of ML engineers is building a real-time fraud detection system. They use Cloud Pub/Sub to stream transactions, Dataflow for feature engineering, and Vertex AI to get predictions. They want to ensure that the data used for training matches the data used for serving to avoid training-serving skew. Which approach should they take?

A company wants to build a model to predict housing prices using BigQuery ML. They have a dataset with features like area, number of bedrooms, and location. Which TWO model types are appropriate for this regression task?

A data scientist needs to retrieve training data from Vertex AI Feature Store that exactly matches the feature values as they were at a specific historical timestamp to avoid label leakage. Which feature view configuration should they use?

A team of data scientists and ML engineers is collaborating on a shared feature store in Vertex AI Feature Store. They need to ensure that feature definitions are versioned and that changes are reviewed before being used in production pipelines. Which TWO practices should they implement?

A logistics company uses Vertex AI AutoML Tables to predict delivery delays based on order attributes, weather data, and traffic data. The model is retrained weekly using a Vertex AI Pipeline that runs a BigQuery query to get training data, then triggers AutoML training. Recently, the pipeline fails with the error 'Dataset not found' when the AutoML training step starts. The BigQuery query runs successfully and outputs a table. Which is the most likely cause?

A data science team is deploying a PyTorch model for real-time inference using Vertex AI Endpoints. The model requires a custom container with specific CUDA drivers and Python packages. They have created a Docker image and pushed it to Artifact Registry. The pipeline should automatically retrain the model every week and deploy the new version if it passes validation. However, the deployment step fails intermittently with the error 'The container image is not compatible with the machine type.' What is the most likely cause?

An organization wants to collect ground truth labels for model quality monitoring and store them in BigQuery. They also want to compute and visualize a confusion matrix over time. Which TWO actions should they take? (Choose 2)

You are designing a batch prediction pipeline using Vertex AI. The input data is 50 TB in CSV format on GCS. The model requires feature engineering that involves complex transformations (e.g., datetime parsing, one-hot encoding). Which TWO services or steps should you include in your pipeline?

A company needs to detect objects in live video streams from security cameras. They require low-latency predictions and want to minimise operational overhead. Which TWO services should they use? (Choose 2)

A global e-commerce company uses BigQuery ML to forecast daily sales for 10,000 products. They use a time-series model with a horizon of 7 days. Recently, forecasts for a specific product category have been consistently too high. They suspect the model is not capturing a new seasonal pattern. Which action should they take first to diagnose the issue?

An MLOps team wants to set up alerts for GPU memory utilization on Vertex AI Training jobs. Which approach is most efficient?

A team is using Delta Lake on Dataproc for their data lake with ACID transactions. They want to version data for ML experiments and roll back to a previous version if needed. Which Delta Lake feature should they use?

A marketing agency uses Vertex AI AutoML Vision to classify social media images into brand logos and generic content. They have 5,000 images per class. The model achieves 95% accuracy on validation set, but in production it misclassifies many images that contain logos in unusual angles or lighting. They have limited ML expertise and want to improve robustness. Which action should they take?

A team is training a large TensorFlow model that requires more memory than a single GPU provides. They have access to multiple GPUs on a single machine. Which distributed training strategy should they use to split the model layers across GPUs?

What is the primary benefit of using a centralised model registry in MLOps?

Your ML pipeline uses Vertex AI Feature Store to serve features for online predictions. You need to monitor the freshness of features in the online store. Which approach is most effective?

A data science team wants to version control their datasets along with code using Git. They need a tool that integrates with Git and tracks changes to large data files. Which tool should they use?

An ML team is using Vertex AI Pipelines to run automated retraining workflows. They want to monitor pipeline execution and receive alerts when a pipeline run fails. Which Google Cloud service should they use to set up such alerts?

Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?

You are using Vertex AI hyperparameter tuning with a custom container. The training job reports the objective metric but Vizier is not converging. Which configuration change could improve convergence?

An ML team wants to automatically retrain a model when prediction drift is detected on the deployed endpoint. They have Vertex AI Model Monitoring configured to send alerts to Cloud Monitoring. Which minimal set of additional services should they use to trigger a retraining pipeline?

A company uses Vertex AI Vector Search (Matching Engine) for a product recommendation system. The product embeddings are updated hourly. Which index update method should they use to ensure low latency for new items?

An ML team is deploying a model to Vertex AI for the first time. Which THREE are best practices for scaling from prototype to production?

A team has developed a prototype of a recommendation model using a small dataset on a single VM. They need to scale to a larger dataset for production training. They plan to use Vertex AI training with a custom container. What is the best practice for handling the increased data volume?

An ML engineer is designing a pipeline that should run only when new training data arrives in a Cloud Storage bucket. Which event-driven approach should they use to trigger the Vertex AI Pipeline?

Your company deploys batch prediction jobs using Vertex AI Batch Prediction. You need to monitor the jobs for failures and performance. What is the recommended approach?

A retail company has deployed a machine learning model using Vertex AI Endpoints to predict inventory demand. The model was trained on data from the past two years and has been in production for six months. The team has enabled Vertex AI Model Monitoring to track prediction drift with an alert threshold of 0.2. Last week, they received an alert that the prediction drift score reached 0.35, exceeding the threshold. The engineer checks the monitoring dashboard and sees that the distribution of predictions has shifted noticeably compared to the training data. The engineer also notices that the model's accuracy metrics, computed from weekly ground truth data, have remained within acceptable range. What should the engineer do first?

A company is using Vertex AI Vizier for hyperparameter tuning of a model with 5 integer hyperparameters, each with a range of 10-100. They have a budget of 50 trials and want to maximize the chance of finding the best configuration. Which Vizier algorithm should they use?

An ML engineer wants to containerize a custom training script and use it as a component in a Vertex AI Pipeline. The component should accept a dataset URI and a learning rate parameter, and output a trained model artifact. Which approach should the engineer use to define the component?

A team wants to deploy a BigQuery ML model for online prediction. Which approach should they take?

A team is deploying a new model version. They want to ensure that they can quickly roll back if the new version performs poorly in production. Which TWO actions should they take? (Choose 2.)

A company has multiple business units using the same Vertex AI environment. They need to enforce that models deployed to production have passed a validation pipeline, and only the ML Engineering team can deploy to production. Which IAM configuration should they use?

A team deploys a model using Vertex AI and wants to monitor for concept drift. What should they track?

Which TWO are best practices for building ML pipelines on Vertex AI Pipelines?

Which TWO are best practices when deploying AutoML models to production?

A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?

An ML engineer needs to track the costs incurred by Vertex AI prediction endpoints. Which tool should they use to set budget alerts and monitor spending?

You need to set up monitoring for a Vertex AI model that serves predictions in real-time. The model is expected to have a latency SLA of under 100ms. Which metric should you configure an alert on to ensure the SLA is met?

A machine learning engineer wants to use a pre-built Google Cloud Pipeline Components (GCPC) to train a model using Vertex AI. Which component should they use?

You are an ML engineer at a logistics company. You have deployed a deep learning model on Vertex AI Endpoints using a custom container with GPU acceleration. The model predicts delivery times based on route features. After one week, you notice that the endpoint's GPU utilization is consistently at 10%, but the prediction latency has increased by 50%. The number of prediction requests per second has remained stable. You check the container logs and see no errors. The model is served using TensorFlow Serving with batching enabled (batch size: 32, batch timeout: 100ms). The custom container uses a single NVIDIA T4 GPU. You have also set the Vertex AI endpoint to use autoscaling with minReplicaCount: 1 and maxReplicaCount: 5, and the CPU utilization target is 60%. Which action should you take to reduce latency?

A company has deployed a TensorFlow model on Vertex AI Prediction for real-time inference. They notice that during peak hours, the prediction latency increases significantly, and some requests time out. The model requires GPU acceleration. Which action should they take to reduce latency and avoid timeouts?

A data scientist has a TensorFlow 2.x model trained on a single GPU. They want to scale training to multiple GPUs on a single Vertex AI machine without code changes. Which strategy should they use?

A startup wants to add sentiment analysis to their customer feedback app without any labeled data or custom model training. Which Google Cloud service should they use?

Which Vertex AI service allows you to discover, fine-tune, and deploy foundation models with a few clicks, including models like Llama and Gemma?

A machine learning engineer wants to deploy a trained model to Vertex AI for online predictions. Which Vertex AI resource is required to serve the model and provide an endpoint URL?

A team trains a distributed TensorFlow model using the config above. After training, they deploy the model for online predictions. The model returns poor quality predictions. They suspect that the model was not trained correctly due to a configuration error. What is the most likely mistake?

An ML engineer needs to set up automated retraining triggered by data drift. They have decided to use Cloud Monitoring alerts to detect drift. Which TWO additional services are required to complete the retraining pipeline? (Choose 2)

An ML engineer is deploying a large BERT-based natural language processing model for real-time inference on Vertex AI Prediction. The model has a large memory footprint (2GB) and experiences unpredictable traffic spikes up to 10x the baseline. The engineer needs to minimize latency and cost while handling spiky traffic. Which TWO actions should the engineer take? (Choose two.)

Your Vertex AI endpoint receives many identical prediction requests (same input features). You want to cache responses to reduce latency and cost. Which Google Cloud service should you use?

You are an ML engineer at a large e-commerce company. Your team has developed a product recommendation model using TensorFlow and deployed it on Vertex AI Endpoints for real-time inference. The model is retrained weekly using a Vertex AI Pipeline that reads new user interaction data from BigQuery, trains the model, evaluates it, and deploys the new version to the endpoint with a traffic split: 10% to the new model and 90% to the previous champion model. Recently, the team noticed that the new model's online prediction latency has increased significantly (from 50ms to 200ms) after deployment, causing timeouts for some requests. The training code has not changed, and the model size is similar. The pipeline uses a custom container with the same TensorFlow Serving image as before. The deployment step uses the same machine type (n1-standard-4) for the endpoint. What is the most likely cause of the latency increase?

The exhibit shows part of a Vertex AI Pipeline definition. The pipeline fails at the training step with an error: 'Missing required input: train_data'. What is the most likely cause?

Which TWO of the following are best practices for versioning ML models and datasets?

A financial services firm uses Vertex AI AutoML Natural Language to classify customer feedback into categories (positive, neutral, negative). They notice that the model performs poorly on neutral and negative classes, with high false negatives for negative. The dataset has 10,000 samples: 8,000 positive, 1,000 neutral, 1,000 negative. They have trained the model with automatic data split and default hyperparameters. Which course of action should they take to improve classification of minority classes?

A healthcare startup deployed a Vertex AI AutoML Vision model to detect anomalies in medical images. The model performs well on the test set but has high latency in production, exceeding the 2-second SLA. The images are stored in Cloud Storage and are processed via a Cloud Function triggered by new uploads. What is the most likely cause?

Match each optimization algorithm to its characteristic.

Refer to the exhibit. What is the purpose of this query?

Your company runs a high-traffic web application that serves the same machine learning model prediction for many identical requests (e.g., product recommendations for the same user profile). You want to reduce latency and load on the prediction endpoint by caching responses. Which Google Cloud service should you use?

A data scientist wants to quickly experiment with a pre-trained Vision Transformer model from Hugging Face and fine-tune it on a custom dataset using Vertex AI. They want to use a managed environment with minimal setup. Which Vertex AI service should they use?

You need to deploy a model that requires a large amount of memory (over 200 GB) for inference. The model is a custom PyTorch model. Vertex AI endpoints have machine type limitations. Which TWO actions can you take to handle this memory requirement? (Choose 2 correct answers)

A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?

Your team deploys a model using Vertex AI Endpoints with autoscaling. Which TWO metrics are most important to monitor in order to optimize cost and performance? (Choose two.)

A data analyst wants to build a binary classification model using a low-code ML solution on Google Cloud. The dataset is stored in BigQuery and contains 500,000 rows with 20 features, including categorical and numerical columns. The analyst has minimal coding experience and needs to deploy the model as an API endpoint for real-time predictions. Which two Google Cloud services should the analyst use to accomplish this task with minimal code? Choose two options.

A company wants to transcribe customer service calls in real-time to detect sentiment and identify urgent issues. They need a solution with low latency. Which combination of pre-built APIs should they use?

A team uses Vertex AI Pipelines with CustomJob components that pull training code from a Cloud Source Repository. The pipeline fails with a 'Permission denied' error when trying to access the repository. The service account used by the pipeline has the 'Source Repository Viewer' role. What is the likely issue?

A team wants to deploy a model on Vertex AI Edge Manager for offline inference on edge devices. Which three steps are required? (Choose 3)

A company uses Cloud Composer to orchestrate their ML pipelines. They notice that tasks are being queued but not executed, causing delays. What is the most likely cause?

You are deploying a model to a Vertex AI endpoint and need to minimize latency for online predictions. Which machine type should you choose?

Which THREE actions should be taken to manage model versions effectively?

A data scientist has deployed a classification model on a Vertex AI Endpoint and wants to monitor for feature drift in the serving data compared to the training data. Which Vertex AI service should be used?

A company serves a PyTorch model using a custom container on Vertex AI Prediction. They notice that after a few hours, the endpoint returns 502 errors. The logs show 'Out of memory' errors. The container has a memory limit of 4GB, and the model loads a 3GB vocabulary file. What is the most likely cause and best fix?

You are fine-tuning a pre-trained BERT model from Hugging Face on a custom text classification dataset using Vertex AI Training. You want to speed up training by using mixed precision. What should you do?

Your team manages multiple ML models on Vertex AI. You need to implement a centralized monitoring solution to track model performance over time. Which TWO approaches should you consider? (Choose two.)

A company uses Vertex AI Experiments to track ML training runs. They want to enforce that all training runs use only approved libraries from a central Artifact Registry to ensure compliance. Which approach should they take?

An ML engineer is troubleshooting why a Vertex AI Endpoint is returning high prediction latency. They have enabled request/response logging and see that some requests take >1 second while most are fast. Which THREE actions should they take to diagnose the issue?

An organization needs to implement MLOps with standardized pipeline templates across multiple teams. Which Vertex AI feature should they use to create reusable pipeline components?

A financial services company uses Vertex AI Pipelines to train and deploy models for fraud detection. The ML team consists of data scientists who develop models and ML engineers who deploy them. They use a CI/CD pipeline with Cloud Build to build and push Docker images to Artifact Registry, then trigger Vertex AI Pipelines. Recently, the team noticed that a model deployed to production was trained on a dataset that had not been approved by the data governance team. Upon investigation, they found that a data scientist accidentally used an unapproved version of the training data by specifying a Cloud Storage path that was not the latest approved dataset. The company needs to enforce that only approved datasets are used in training jobs. Which approach should they take?

A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?

A company deploys an online prediction model serving 100 requests per second. They are optimizing for both latency and throughput. Which monitoring strategy should they use?

Which TWO of the following are best practices for managing data in a collaborative machine learning environment on Google Cloud?

A company deploys a model on Vertex AI Prediction with autoscaling enabled. They notice that during a traffic spike, new instances take several minutes to become available, causing high latency. What is the best solution?

Drag and drop the steps to create and deploy a custom ML model on Vertex AI using a container in the correct order.

An organization wants to use Vertex AI JumpStart to fine-tune a foundation model for a custom classification task. They have a labeled dataset stored in BigQuery. Which steps should they take?

A company uses Vertex AI Pipelines for model training and deployment. The pipeline includes a model evaluation step that produces metrics. If the metrics are below a threshold, the pipeline should fail and not deploy. Which component should they use?

A team wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in a Cloud Storage bucket. Which combination of services should they use?

A company wants to monitor fairness of a model by evaluating performance metrics across demographic subgroups. They have ground truth labels stored in BigQuery. Which Vertex AI service should they use?

A team uses Vertex AI Workbench notebooks for collaborative model development. They want to ensure that code changes are version-controlled, that multiple data scientists can work on the same notebook without conflicts, and that the environment is reproducible across team members. Which approach should they take?

An ML team wants to implement data versioning for large datasets stored in Google Cloud Storage. They need to track changes over time and reproduce previous data states. Which tool is most appropriate?

Refer to the exhibit. What is being configured on Vertex AI?

Which THREE are valid uses of Vertex AI Metadata?

The exhibit shows a Cloud Composer environment variable configuration. An ML pipeline DAG fails with an authentication error when trying to access Vertex AI. What is the most likely cause?

A team has a prototype image classification model trained on a small dataset using TensorFlow Keras on a single GPU. They need to train on a larger dataset (1 million images) using a distributed strategy on Vertex AI with 8 GPUs. They implement a MirroredStrategy for data parallelism. During the first few epochs, the training speed does not improve significantly compared to a single GPU, and GPU utilization is low. The data is stored as JPEG files in Cloud Storage, and the input pipeline uses tf.data with map to decode images. What is the most likely cause?

A company uses Vertex AI Predictions with a custom container that invokes an external API for feature enrichment. The prediction response time is highly variable. The engineer wants to monitor the external API's contribution to latency. What should the engineer do?

You are developing a Vertex AI pipeline that runs multiple parallel training jobs with different hyperparameters, then collects their results and selects the best model. Which KFP SDK v2 construct should you use to run the parallel training tasks?

A company wants to build a product recommendation engine for their e-commerce website. They have historical purchase data and user interaction logs. They want a managed service that can quickly generate personalized recommendations without building custom models. Which service should they use?

You need to serve a model on an edge device with low latency and offline capability. Which approach should you use?

A financial services company wants to detect fraudulent transactions in real-time. They have a trained XGBoost model that runs on a single Compute Engine instance. The current solution processes about 100 transactions per second, but they need to scale to 10,000 transactions per second. Which approach should they take?

Which TWO actions are best practices when scaling a prototype ML model to production in Google Cloud?

A team is building a fraud detection model that requires joining real-time transaction features with historical user features. They need to ensure that the training data does not use future information (data leakage). Which Vertex AI Feature Store capability should they use?

A team uses Vertex AI Pipelines and wants to track lineage of artifacts and executions. Which three resources should they use? (Choose three.)

An ML engineer is using Vertex AI Vizier to tune hyperparameters for a PyTorch model. They want to maximise the chance of finding the global optimum within a fixed trial budget of 50 trials. Which algorithm should they select?

A retail company uses Vertex AI Tabular (AutoML Tables) to build a customer churn prediction model. The training dataset contains 50,000 rows and 30 features, with a 5% churn rate. The model achieves an AUC of 0.85 on the test set. When deployed for online predictions, the average latency is 800ms, while the business requirement is under 200ms. The engineer has already reduced the feature set to 10 features, but latency only dropped to 600ms. The model size is 2GB. The endpoint is in us-central1 using an n1-standard-4 machine with minReplicaCount=1. What should the engineer do to meet the latency requirement?

A company is deploying a computer vision model on edge devices using TensorFlow Lite. They want to reduce model size without significant accuracy loss. Which TWO model compression techniques are most suitable?

A real-time recommendation model deployed on Vertex AI Endpoints is experiencing increased latency, especially during peak hours. The model is hosted on a single machine with 4 CPUs. Which set of actions should you take to diagnose and resolve the issue?

Your team is training a very large transformer model that does not fit on a single GPU. They are using Vertex AI custom training with PyTorch. Which distributed training approach should they use?

A company uses AutoML Tables to predict customer churn. The model's AUC is low. Which action is most likely to improve performance?

Refer to the exhibit. What does this query return?

An MLOps team needs to automatically retrain a model when new training data becomes available. They use Vertex AI Pipelines. What is the recommended way to trigger the pipeline?

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

An ML team is using Vertex AI Online Prediction and wants to receive alerts when the 99th percentile latency exceeds 500ms for more than 5 minutes. What is the best practice to set up this alert in Cloud Monitoring?

A data engineering team needs to compute rolling window features (7-day average, 30-day sum) from a high-volume stream of e-commerce events stored in BigQuery. They must output the features to Vertex AI Feature Store for online serving. Which approach is MOST cost-effective and scalable?

You have a champion model serving 100% traffic on a Vertex AI endpoint. You want to deploy a challenger model and gradually shift 10% of traffic to it for A/B testing. What is the correct approach?

A company has a TensorFlow model that uses custom operations compiled as .so files. They want to deploy it on Vertex AI for online predictions. The model runs correctly when loaded locally. However, on Vertex AI, the prediction fails with a 'Op type not registered' error. What is the most likely reason?

Which THREE are best practices for implementing CI/CD for ML pipelines on Google Cloud? (Choose THREE.)

A company uses BigQuery ML to train a boosted tree classifier on a large dataset. After training, they want to understand which features most influence predictions. Which BigQuery ML function should they use?

You have a Vertex AI endpoint with min_replica_count=2 and max_replica_count=10. You notice that during a traffic spike, the endpoint does not scale up quickly enough, causing increased latency. What should you do to improve autoscaling responsiveness?

A data science team has deployed a model on Vertex AI and wants to automatically detect when the distribution of a specific feature shifts significantly from the training data. Which service should they use?

A team is deploying a large PyTorch model for online inference. They want to use NVIDIA Triton Inference Server to optimize serving performance. How can they integrate Triton with Vertex AI?

A company uses Vertex AI for online predictions with a large ensemble model that requires GPU acceleration. They want to reduce inference latency by batching multiple requests into a single GPU inference call. What should they configure?

A company uses Vertex AI Pipelines with Kubeflow DSL for hyperparameter tuning. They notice that some trials fail due to OOM errors. How should they configure the pipeline to automatically handle this?

A machine learning team is deploying a model for real-time predictions using Vertex AI. They need to ensure that the deployment follows best practices for collaboration and governance. Which TWO actions should they take?

An MLOps team is implementing a CI/CD pipeline for a TensorFlow model on Vertex AI. The model training job takes 2 hours and produces a SavedModel. The team wants to automatically trigger a new pipeline run whenever a change is pushed to the 'main' branch of their source repository. The pipeline should include training, evaluation, and if metrics exceed a threshold, deploy the model to a Vertex AI endpoint. Which trigger configuration should they use?

An ML team wants to use Vertex AI Hyperparameter Tuning to tune a custom training job. They have a budget of 50 trials and want to use an algorithm that balances exploration and exploitation. Which algorithm should they choose?

A team uses Vertex AI Feature Store to serve features for online predictions. They notice that the online serving latency is high for certain features. The features are stored in a BigQuery source with high cardinality. What is the best practice to reduce latency?

A data scientist wants to deploy a model trained with PyTorch to a Vertex AI endpoint for online predictions. What is the recommended approach?

You want to use a pre-trained model from TensorFlow Hub for image classification, but you need to adapt it to classify your own custom categories with a small dataset. Which Vertex AI approach is most appropriate?

You want to reduce training costs by using preemptible VMs on Vertex AI for a fault-tolerant distributed training job that uses checkpointing. Which machine type should you choose in the worker pool configuration?

A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?

An ML engineer is optimizing a large model for deployment on Vertex AI with GPU acceleration. They want to reduce model size and improve inference latency without significant accuracy loss. Which tool should they use?

A retail company has been using Vertex AI AutoML to predict store-level demand for each product. They have a pipeline that runs nightly: data is extracted from BigQuery, preprocessed via Dataflow, and then used to train a new AutoML model each night. The model is deployed to a Vertex AI Endpoint for real-time inference. After two months, they notice that predictions for a new product category (recently launched) are consistently inaccurate, with predicted sales far exceeding actuals. They suspect data drift due to the new category. The data scientist has limited coding skills and wants a low-code solution. Which course of action should they take to improve predictions for the new category?

After setting up model monitoring on Vertex AI for a classification model, the engineer sees a high number of anomaly alerts for the "age" feature. Upon investigation, the age distribution in recent predictions is similar to training data. What might be the cause?

You are setting up a hyperparameter tuning job on Vertex AI for a large neural network. The objective is to minimize validation loss. You want to explore the hyperparameter space efficiently with a limited budget of 100 trials. Which THREE settings should you configure in the study?

A team has deployed a model on Vertex AI and wants to cache frequent identical prediction requests to improve latency and reduce cost. Which Google Cloud service should they use?

A large e-commerce company deploys multiple ML models on Vertex AI Endpoints. They use Vertex AI Model Registry to manage model versions. Recently, a team accidentally deployed an unvalidated model to production, causing a service outage. They want to implement a governance process where models must pass certain validation checks before deployment. The validation includes unit tests, fairness checks, and performance benchmarks. They use CI/CD pipelines (Cloud Build). They also need to allow manual approval for critical models. Which combination of Vertex AI features and Cloud Build steps would enforce the required governance?

You are deploying a pre-trained BERT model for inference on edge devices. The model must be under 500 MB and inference latency under 50 ms. Which approach should you take?

You are using tf.Transform to preprocess data at scale. Which TWO services are required to run tf.Transform on Google Cloud? (Choose 2)

A company has a CI/CD pipeline that retrains a model every time new training data is available. They want to automatically deploy the new model to production only if it passes a set of evaluation tests on a staging environment. Which approach best implements this?

Your team is serving a large language model on Vertex AI using a custom container. The endpoint experiences intermittent 502 errors during traffic spikes. The autoscaling configuration uses a CPU utilization target of 60% and the model is deployed on n1-standard-4 instances. The model requires significant memory. Which combination of changes is most likely to resolve the issue?

A data scientist wants to use tf.Transform for preprocessing a large dataset stored in BigQuery before training a TensorFlow model. The preprocessing should be consistent during training and serving. What is the correct way to use tf.Transform in this scenario?

A developer creates a Cloud Build trigger that runs a training pipeline whenever code is pushed to the main branch of the repository. The trigger is configured to use a source archive stored in Cloud Storage. After pushing code to main, the build fails with the error shown. What is the most likely cause of this failure?

A company deploys a model on Vertex AI Endpoint and expects high traffic spikes during promotional events. The current configuration uses manual scaling with 2 replicas. Which autoscaling configuration should they use to handle spikes while minimizing cost during normal traffic?

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?

A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?

A team has a Vertex AI pipeline that includes a container component for data preprocessing. The team notices that the component is re-executed every time the pipeline runs, even when the inputs and code haven't changed. They want to leverage pipeline caching to avoid redundant executions. What should they do to enable caching for this component?

A team is responsible for monitoring the health of a Vertex AI pipeline that runs daily. Which THREE resources should they use to gain visibility into pipeline performance and failures? (Choose 3.)

A data science team uses a shared Cloud Storage bucket to store training datasets. They notice that some team members accidentally overwrite existing datasets, causing issues with reproducibility. Which approach best prevents accidental overwrites while maintaining collaboration?

A company wants to run batch predictions on millions of records stored in BigQuery. They need to preprocess the data (e.g., feature engineering) before feeding it to the model. Which approach is most scalable and cost-effective?

You are responsible for maintaining an ML pipeline that runs daily on Vertex AI Pipelines. The pipeline preprocesses data, trains a model, and deploys it to an endpoint. Recently, the pipeline has been failing at the deployment step because the endpoint already exists and the deploy step tries to create a new endpoint instead of updating the existing one. The pipeline code is written using the Kubeflow Pipelines SDK. You need to modify the pipeline to resolve this issue with minimal changes. What should you do?

A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is served using TensorFlow Serving on a Google Kubernetes Engine (GKE) cluster with auto-scaling enabled. The cluster uses n1-standard-4 machine types. The team has set up Cloud Monitoring dashboards and alerts. Last week, during a major holiday promotion, the team noticed that the model's inference latency P99 increased from 150 ms to 450 ms over a 30-minute period, while the request throughput increased from 500 to 1,200 requests per second. CPU utilization across the cluster rose to 95%, but memory utilization remained at 60%. The model version and the serving infrastructure configuration have not changed since the last deployment. Which action should the team take to mitigate the latency issue?

An MLOps engineer needs to collect ground truth labels for a deployed classification model to compare predictions against actuals. Where should the engineer store the ground truth data to enable Vertex AI model quality monitoring?

A data science team is collaborating on a project to build a churn prediction model. They use Vertex AI Workbench instances for development. Each data scientist has their own instance with a persistent disk. They share code via a GitHub repository. They want to ensure that the model training is reproducible across different team members' environments. Currently, they manually install Python packages in their instances, and they have noticed that the model metrics differ slightly between runs on different instances. Which of the following is the best action to ensure reproducibility?

A machine learning team deploys a PyTorch model for online prediction on Vertex AI using a custom container. They notice that the first few requests after scaling up experience high latency. What is the most likely cause and how should they mitigate it?

A company uses Vertex AI Model Monitoring to detect data drift. They have a model that predicts house prices. Which dataset should they compare against the training data to detect drift?

A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this failure?

A company has a prototype ML model that predicts equipment failure. They want to deploy it to production using Vertex AI. The model must be retrained weekly with new data. They also need to monitor for data drift and model performance. Which THREE components should they include in their MLOps pipeline? (Choose 3)

You want to use Vertex AI Vizier for hyperparameter tuning. You have 2 categorical parameters and 3 continuous parameters. Which algorithm is best suited for this mixed parameter space?

A team is scaling their prototype inference model to handle high-throughput requests with low latency. They use a custom container on Vertex AI Prediction. They notice that latency spikes occur under heavy load. What is the most effective strategy?

A team is serving a large language model (LLM) on Vertex AI using a custom container. They want to reduce tail latency. Which THREE strategies should they consider?

An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?

A data science team wants to build a machine learning pipeline on Vertex AI Pipelines that preprocesses data, trains a model, and evaluates it. They need to ensure that components can be reused across multiple pipelines and that outputs from one component can be passed as inputs to another. Which approach should they take?

Match each feature engineering technique to its description.

A data engineering team wants to orchestrate an ML pipeline that includes data preprocessing in Dataflow, AutoML training, and model deployment. They want to minimize operational overhead. Which approach is best?

A team just moved a model from prototype to production using Vertex AI. They notice prediction errors for certain inputs that were not present in training data. What should they do to detect such issues automatically?

A retail company wants to build a recommendation system to show 'frequently bought together' items. Which Recommendations AI model type should they use?

A pharmaceutical company uses Vertex AI Pipelines with custom training containers. Recently, the pipeline has been failing with 'Container failed with exit code 137' (out of memory). The container runs with default memory limit. The team needs to fix this without changing the code. The project quota for CPU and memory is sufficient. What should the team do?

A team of data scientists is collaborating on notebooks in Vertex AI Workbench. They need to use Git for version control and share notebooks with real-time editing. Which type of Workbench instance should they choose?

An ML team wants to automatically retrain a model when data drift is detected. They have set up a Cloud Monitoring alert on drift. What service should they use to trigger a retraining pipeline in response to the alert?

Match each regularization technique to its effect.

Drag and drop the steps to set up a batch prediction job using Vertex AI in the correct order.

A machine learning engineer needs to create a pipeline that runs a custom container component on Vertex AI. The container expects a Cloud Storage path as input and outputs a model artifact. Which component type should they define using the Kubeflow Pipelines SDK v2?

Which TWO services are commonly used together to implement an end-to-end ML pipeline that automatically retrains and deploys models on Vertex AI? (Choose two.)

A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?

A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive data stored in BigQuery that includes protected health information (PHI). The data science team uses Vertex AI Workbench notebooks to explore the data and develop models. The organization's security policy requires that all PHI data must be encrypted at rest and in transit, and that access to the data is logged and audited. They also need to ensure that the data used for model training is de-identified to remove direct identifiers such as patient names and SSNs. The team wants to automate the de-identification process as part of the data pipeline. Which approach meets these requirements?

A data science team is configuring Vertex AI Model Monitoring for a deployed model. They want to detect both feature skew and feature drift. Which TWO configurations must they set?

Refer to the exhibit. The team notices that the pipeline fails to read data from the specified Cloud Storage path. What is the most likely issue?

Refer to the exhibit. An engineer notices no drift alerts but the model performance has degraded. What is the likely cause?

A team wants to serve a large PyTorch model (3 GB) for online predictions with low latency. Which THREE actions should they take?

An ML engineer runs this command to upload a model. The model artifact in Cloud Storage is a directory containing model.pkl and a custom preprocessing script. What will happen when he later deploys this model to an endpoint and sends a prediction request?

A data science team wants to build a Vertex AI pipeline that trains a model, evaluates it, and conditionally deploys it if the accuracy exceeds 0.9. They want to use the Kubeflow Pipelines SDK v2. Which construct allows them to conditionally execute the deployment step based on the evaluation metric?

Which TWO of the following can be used as input sources for Vertex AI batch prediction jobs? (Choose 2)

A company deploys a model on Vertex AI Prediction for real-time inference. Users report intermittent high latency during peak hours. The model is deployed on a single machine type with `min_replica_count=1` and `max_replica_count=5`. Autoscaling is enabled based on CPU utilization. What is the most likely cause of the latency spikes?

A company wants to use Vertex AI Vector Search for real-time product recommendations based on user embeddings. They need to update the index frequently with new product embeddings without significant downtime. Which TWO options should they consider? (Choose 2)

What is the primary benefit of using pipeline caching in Vertex AI Pipelines?

An ML team is building a feature pipeline with Dataflow that reads from BigQuery, computes features, and writes to Vertex AI Feature Store. They need to ensure that features are available for both training and serving with low latency. Which Feature Store option should they use?

A company has deployed a computer vision model on Vertex AI Prediction using a custom container. The model processes high-resolution images and serves predictions to a mobile application. Recently, users have reported that predictions sometimes take over 10 seconds, and the application times out. The ML engineer's monitoring shows that the endpoint's CPU utilization is consistently high (above 85%) and that the request latency spikes during peak hours. The model is deployed on n1-standard-4 machines with automatic scaling set to minReplicaCount=1 and maxReplicaCount=5. The engineer has observed that the endpoint rarely scales beyond 2 replicas even during peak hours. What should the engineer do to reduce prediction latency?

A manufacturing company wants to predict equipment failure using sensor data stored in BigQuery. They have limited ML expertise and want to use AutoML Tables. The data includes timestamps, numerical sensor readings, and a boolean 'failure' column. The dataset is highly imbalanced with only 1% failure cases. Which of the following is the most effective approach to handle the imbalance in AutoML Tables?

A company wants to use low-code ML for time series forecasting with 5 years of hourly data. They need to incorporate holiday effects. Which solution best meets these requirements?

Your team deploys a multi-model endpoint on Vertex AI with two models: Model A (small, low latency) and Model B (large, high latency). You configure traffic splitting so that 90% goes to Model A and 10% to Model B. However, you notice that the latency for Model A increases when Model B receives traffic. What is the most likely cause?

An engineer wants to use BigQuery ML to explain predictions from a trained boosted tree classifier for a specific set of input rows. Which function should they use?

A startup is deploying its first machine learning model using BigQuery ML. The model is a logistic regression for churn prediction, trained on a dataset of 5 million rows. The pipeline runs every week: it exports training data from BigQuery, trains a model using BigQuery ML, and then deploys the model as a remote model for predictions. The ML engineer wants to set up basic monitoring to ensure the pipeline runs successfully and the model quality does not degrade. Which monitoring approach should the engineer implement first?

You are designing a distributed training job for a very large neural network that does not fit on a single machine. You need to split the model across multiple devices. Which TWO techniques can you use?

A company wants to be alerted when the prediction error rate on their Vertex AI Endpoint exceeds 5% in any 5-minute window. What is the best way to set up this alert?

A data science team wants to deploy a ML pipeline on Vertex AI Pipelines that includes a component to train a model using a custom container. The component should be reusable across different pipelines and accept hyperparameters as inputs. Which approach should they take?

Your model serving endpoint on Vertex AI is experiencing increased memory usage after a recent update. The model was converted from TensorFlow to TF Lite for faster inference. You notice that the endpoint's instances occasionally get killed due to out-of-memory (OOM) errors. What is the most likely cause?

You are optimizing a model for deployment on Vertex AI using NVIDIA Triton Inference Server. Which TWO actions can you take to improve inference performance?

A data science team uses Vertex AI Experiments to track training runs. They want to automatically log parameters, metrics, and artifacts for all runs with minimal code changes. Which approach should they take?

You are defining a Python function component in KFP SDK v2. Which decorator should you use?

An engineer is training a model on Vertex AI using a custom container. The training job fails with an error indicating that the container exited with a non-zero status. The engineer wants to debug the issue. What is the best way to access the logs?

Drag and drop the steps to set up model monitoring for drift detection on Vertex AI in the correct order.

A team develops a pipeline that trains a model and evaluates it. They want to pass the test accuracy (a float) from the evaluation component to a subsequent deployment component. Which KFP SDK type should the evaluation component output be annotated with?

Drag and drop the steps to perform a hyperparameter tuning job on Vertex AI in the correct order.

A team is monitoring a model on Vertex AI Endpoints and wants to track the p99 latency of online predictions. Which approach should they use to set up latency monitoring and alerting?

A company is deploying a deep learning model on edge devices with limited storage and computational resources. They need to reduce the model size by 80% while maintaining acceptable accuracy. Which two techniques should they combine?

Which TWO factors should you consider when choosing between BigQuery and Cloud Storage for storing training data? (Choose 2)

An ML engineer trained a model and registered it in Vertex AI Model Registry. They want to assign the alias 'champion' to the best-performing version for production deployment. Which gcloud command should they use?

A company wants to use Vertex AI for hyperparameter tuning. Which three components are required to configure a hyperparameter tuning job? (Choose THREE.)

You have a TensorFlow model that you want to deploy on edge devices for real-time inference. The model was trained in Vertex AI. You need to convert it to a format suitable for on-device inference. Which approach should you use?

A company is building a document processing pipeline for invoices. They need to extract key fields (invoice number, date, total amount) and allow human review for invoices over $10,000. Which TWO Google Cloud services/features should they combine?

You need to serve multiple models on a single Vertex AI endpoint to reduce costs. How can you achieve this?

A machine learning team has a prototype using a custom TensorFlow model trained on a small dataset stored in Cloud Storage. They want to scale the prototype to production with minimal code changes while ensuring the model can handle increased traffic and new data. The model currently loads data using tf.data.Dataset from CSV files. Which approach best meets these requirements?

A data scientist wants to deploy a trained TensorFlow model to Vertex AI for online predictions. They need to serve predictions with low latency and want to leverage GPU acceleration. Which machine type should they select when creating the Vertex AI endpoint?

A company is serving a model for their e-commerce website. They expect traffic to be low at night and very high during flash sales. They want to minimize costs while ensuring availability during spikes. Which autoscaling configuration should they use?

A company has a prototype ML model that works well on historical data, but when deployed to production, the model performance degrades over time. The data distribution shifts gradually. Which strategy should they implement to maintain model accuracy?

A data analyst wants to train a linear regression model to predict house prices using only SQL queries on BigQuery. Which BigQuery ML model type should they use?

A developer needs to transcribe phone calls with high accuracy for a call center analytics application. The audio is in English and has background noise. Which Speech-to-Text model should they choose?

A company has deployed a fraud detection model on Vertex AI Prediction. After three months, the model's accuracy has degraded, and the business is losing money due to undetected fraud. What should the team implement to proactively detect such issues?

A data science team wants to monitor model quality by comparing predictions against ground truth labels. They have deployed a model on Vertex AI Endpoints and enable request/response logging to BigQuery. Which THREE actions should they take to set up model quality monitoring? (Choose 3)

A company uses Vertex AI Pipelines to orchestrate ML workflows. After a pipeline run, they want to query the lineage of a particular model artifact to find out which dataset and hyperparameters were used to produce it. Which API method should they use?

You have a Vertex AI endpoint with autoscaling enabled. You notice that during traffic spikes, the endpoint takes a long time to scale up, causing prediction errors. What is the most effective solution?

You need to run a custom training job on Vertex AI using a pre-built container for scikit-learn. Which container image should you specify?

A team notices that a Vertex AI Pipeline step re-executes every time the pipeline runs, even though its inputs and code have not changed. They want to enable caching for this component to avoid redundant computation. However, caching is currently disabled globally. Which configuration change will enable caching for that specific component?

An organization wants to deploy a pre-trained BERT model for sentiment analysis on Vertex AI. They want to fine-tune it on their domain-specific data. Which feature in Vertex AI allows them to find and fine-tune a suitable foundation model with minimal effort?

Match each ML acronym to its definition.

A machine learning team uses Vertex AI Pipelines for model training. They want to implement a conditional step that runs additional evaluation if the model accuracy exceeds 0.9, otherwise it runs a data augmentation component. Which two Kubeflow Pipelines SDK v2 constructs can they use to achieve this? (Choose two.)

You are running a Vertex AI custom training job with pre-built TensorFlow container. You want to use TPU v3 pods for faster training. Which configuration is required?

A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a large catalog and millions of users. The initial deployment works well for active users, but they notice that new users (with no purchase history) receive generic recommendations that are not personalized. The company wants to improve the cold-start experience. They have user demographic data (age, location) available at sign-up. Current recommendation model is a collaborative filtering model using the built-in Vertex AI Recommendations. What should the company do to improve personalization for new users?

You are deploying a PyTorch model on Vertex AI and want to use NVIDIA Triton Inference Server for optimal performance. You have built a custom container with Triton. Which serving configuration should you use?

A team is operationalizing a machine learning pipeline using Vertex AI. They want to automatically track experiment runs, log model parameters and metrics, and store model artifacts for reproducibility. They also need to capture lineage between pipeline components (e.g., which dataset and hyperparameter tuning job produced a model). Which TWO services should they use together to achieve this? (Choose two.)

Match each ML model interpretability method to its description.

Refer to the exhibit. A user receives the error shown when trying to upload a model to Vertex AI. What is the most likely cause?

An ML engineer has a model trained in Vertex AI and wants to deploy it to an endpoint with autoscaling and traffic splitting for canary testing. They have the model artifact stored in Vertex AI Model Registry with alias 'champion'. What is the correct sequence of steps?

Refer to the exhibit. A user attempts to upload a model to Vertex AI Model Registry using the gcloud CLI. The command fails with the error shown. What is the most likely cause?

A team wants to perform hyperparameter tuning on a Vertex AI custom training job with 100 trials. They require an algorithm that efficiently explores the search space by learning from previous trials. Which algorithm should they select in the study configuration?

A data scientist wants to use AutoML to classify images of retail products into categories. There are 50 categories and the dataset has 100,000 labelled images. Which Vertex AI AutoML service is most appropriate?

A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?

A team deploys a real-time model using a custom container on Vertex AI Prediction. The container is large (5 GB) and cold starts are causing latency spikes. The endpoint is configured with `min_replica_count=0` to reduce cost. The team wants to keep the cost low while reducing cold starts. What is the best approach?

A company uses Cloud Composer to orchestrate their ML workflows. They have an Airflow DAG that runs a Vertex AI pipeline, then a BigQuery query, then a Dataflow job. The DAG is failing because the Vertex AI pipeline takes longer than the Airflow task timeout. What is the best way to handle this?

An ML engineer needs to trigger a Vertex AI Pipeline on a recurring schedule, every 24 hours, to retrain a model with the latest data. Which approach should they use to set up this schedule?

A company trains a model using features from Vertex AI Feature Store. They notice training-serving skew because the feature values used at training time differ from those served online. How should they address this?

A data analyst wants to use low-code ML to analyze text data. Which TWO Google Cloud services are appropriate?

A company is implementing MLOps with Vertex AI. They need to ensure that only approved models can be deployed to production. Which TWO practices should they adopt?

A machine learning engineer wants to manage multiple model versions and facilitate collaboration across teams. The goal is to track model lineage, versioning, and approvals. Which Vertex AI service should they use?

A team is implementing CI/CD for ML using Cloud Build. They want to trigger a training pipeline in Vertex AI whenever a new model code is pushed to the main branch of the repository. Which Cloud Build configuration should they use to achieve this?

You run the above command to deploy a new model version to an existing endpoint. After deployment, you observe that the endpoint's previous model version is still receiving 100% of traffic. What is the most likely reason for this?

A developer sees this error when calling the endpoint. What is the most likely cause?

You are building a CI/CD pipeline for an ML model using Cloud Build. When code is pushed to the main branch, you want to automatically build a training image, run a Vertex AI pipeline, and if the model evaluation passes, deploy it to a staging endpoint. Which two components are essential for this CI/CD pipeline?

You deployed a model to a Vertex AI endpoint with minReplicas=0 and maxReplicas=5. After sending prediction requests, you notice the endpoint takes about 30 seconds to respond initially, but subsequent requests are fast. What is the most likely cause?

An ML engineer is monitoring a Vertex AI Endpoint and notices a spike in 5xx error rates. Which TWO metrics should they examine to diagnose the issue? (Choose 2)

A company deploys a training pipeline on Vertex AI using custom containers. The pipeline includes a hyperparameter tuning job that uses Bayesian optimization. After several runs, they observe that the tuning job is not converging and the search space is large. They want to reduce the number of trials while still finding good hyperparameters. Which strategy should they use?

Which THREE of the following are recommended practices for model governance and lineage in Vertex AI?

An ML engineer is building a pipeline on Vertex AI Pipelines and wants to pass a dataset artifact from one component to another without incurring additional cost for intermediate storage. How should they define the input and output types?

Which TWO options are best practices for building ML pipelines on Vertex AI?

A startup wants to deploy a small machine learning model for real-time predictions but has a very limited budget. Traffic is minimal and predictable. They want to avoid paying for idle resources. Which serving option is most cost-effective?

A model serving team notices that during a flash sale, a real-time recommendation model experiences sudden spikes in traffic, causing some requests to time out. The endpoint is configured with `min_replica_count=3`, `max_replica_count=10`, and autoscaling metric set to `target_utilization=0.6` on CPU. Despite this, autoscaling is too slow. What change will most improve the autoscaling responsiveness?

A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

You are using Vertex AI Vector Search to find nearest neighbors for a recommendation system. Your index is built on 10M embeddings and you need low-latency queries. You want to ensure that adding new embeddings does not require a full index rebuild. Which index type should you use?

You are fine-tuning a large language model using Vertex AI Training with spot VMs to reduce cost. Your training job keeps getting preempted, causing delays. Which THREE strategies can help mitigate the impact of preemption?

An ML engineer has set up Vertex AI Model Monitoring on an endpoint with a sampling rate of 0.1 (10%). They notice that the monitoring job runs hourly but the reported drift metrics seem inconsistent. What is the most likely cause?

Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)

An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

Refer to the exhibit. A data scientist runs the above BigQuery ML query to create a logistic regression model. After training, the model is evaluated using ML.EVALUATE. The evaluation shows poor performance with high bias. Which action would most likely improve the model's performance?

A data scientist is using Vertex AI Experiments to track training runs. They want to automatically log all hyperparameters, metrics, and model artifacts without modifying their training code. Which approach should they use?

A company uses Vertex AI Matching Engine for a product recommendation system. They need to update the index with new product embeddings every hour, but the index is used for online queries with low latency. Which index update strategy should they use?

You are using Vertex AI Matching Engine (Vector Search) to serve similarity search for an e-commerce product recommendation system. The index is updated daily with new product embeddings via a batch job. However, you notice that some new products are not appearing in the search results for up to 24 hours. You need to ensure that new products are discoverable within 1 hour of ingestion. What should you do?

An organization wants to trigger a Vertex AI pipeline whenever a new commit is pushed to the main branch of their Cloud Source Repository. The pipeline should retrain and evaluate the model. Which service should they use to detect the push event and start the pipeline?

A fintech company needs to deploy a TensorFlow model for real-time fraud detection with strict latency SLO (p99 < 100ms). They expect variable traffic with spikes. They also want to minimize cold-start latency. Which two configurations should they use? (Choose 2)

A team is fine-tuning a large language model (LLaMA 2) using Vertex AI with a custom container on a multi-node GPU cluster. They need to implement model parallelism to fit the model across multiple GPUs because it does not fit into a single GPU memory. Which distributed training strategy should they use?

You have deployed a text classification model using Vertex AI Endpoints. The model is performing well, but the operations team wants to be alerted if the endpoint returns an excessive number of HTTP 503 errors. What is the simplest way to achieve this?

A team deploys a model on Vertex AI that uses a custom prediction routine (CPR) with a dependency on a native library. The container crashes with 'ImportError: libcudart.so.11.0: cannot open shared object file'. How should they resolve this?

A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?

You are using Vertex AI Training to train a model and then automatically deploy the best candidate to a Vertex AI Prediction endpoint via the Vertex AI Model Registry. However, after deployment, you notice that the endpoint returns predictions for the new model, but they are significantly different from the evaluation metrics computed during training. The training scripts used TensorFlow with a serving input function. What is the most likely issue and how would you fix it?

Refer to the exhibit. A data analyst creates a BigQuery ML logistic regression model for churn prediction. The model evaluation shows high precision but low recall. Which change to the model creation would most likely improve recall?

Which machine type is most suitable for a Vertex AI endpoint serving a GPU-accelerated model?

You have a Vertex AI endpoint with two deployed models: model A (champion) and model B (challenger). Traffic split is 90:10. You want to gradually increase model B's traffic to 50% over a week. What is the best way to update the traffic split?

Your team has deployed a text classification model on Vertex AI Endpoints. You notice that the model's latency has increased significantly over the last week, but the request rate has remained stable. Which of the following is the most likely cause?

Refer to the exhibit. A team uses this Cloud Build configuration to deploy a model to a Vertex AI endpoint. The build succeeds up to the 'upload' step, but the 'deploy-model' step fails with an error that the model 'my-model' does not exist. What is the most likely cause?

A data scientist trained a model on historical data from 2020-2022 and deployed it in January 2023. In February 2023, the model's accuracy drops significantly. Which monitoring metric would most likely indicate the root cause?

Drag and drop the steps to set up a BigQuery ML linear regression model for forecasting in the correct order.

A company wants to implement a central model governance strategy using Vertex AI. They need to track model lineage, store evaluation metrics, and manage model versions across teams. Which THREE Vertex AI services should they use? (Choose 3)

A company wants to reduce costs for serving a model on Vertex AI Prediction without sacrificing availability. Which THREE strategies should they consider?

Which THREE factors are critical when designing a model serving architecture for a global user base with strict latency SLAs? (Choose 3.)

An engineer needs to deploy multiple models on a single Vertex AI endpoint with separate traffic allocations. What is the maximum number of deployed models that can be assigned traffic on one endpoint?

A data scientist has trained a scikit-learn model locally and wants to deploy it to Vertex AI for online predictions with low latency. The model is a small RandomForestClassifier (100 MB). What is the recommended way to deploy this model?

Your team has built a low-latency similarity search service using Vertex AI Matching Engine (Vector Search). The index is updated daily with new embeddings. You need to serve the latest index without downtime. What is the correct deployment strategy?

An ML team is designing an automated pipeline to retrain a recommendation model every day using new user interaction data stored in BigQuery. The pipeline must be cost-efficient, scalable, and require minimal manual intervention. Which two approaches should they consider?

You have a model that requires GPU for efficient inference. You deploy it on Vertex AI with a single NVIDIA T4 GPU accelerator and notice that the GPU utilization hovers around 30%. The endpoint has 10 replicas. What is the best way to improve cost efficiency while maintaining throughput?

An ML engineer needs to deploy a model from Vertex AI Model Registry to an endpoint. The model has multiple versions. They want to designate one version as the 'champion' for production traffic. How should they do this?

Refer to the exhibit. A data scientist notices that predictions from a deployed model are taking longer than expected. Which Cloud Monitoring metric should be inspected first to identify the bottleneck?

A team uses Vertex AI Metadata to track pipeline runs. They need to identify all artifacts that were generated by a particular pipeline execution. Which API method should they use?

A machine learning engineer is training a large-scale text classification model using a distributed strategy on TPUs. The training loss decreases normally but the validation loss starts increasing after a few epochs while training loss continues to decrease. The engineer suspects overfitting. Which technique is most appropriate to address this while scaling training?

A company deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?

An ML engineer needs to monitor a deployed model for data drift. They want to compare the distribution of incoming predictions against a baseline distribution. Which Vertex AI service should they use?

Refer to the exhibit. What is this Cloud Build step doing?

A company is using AutoML Vision for object detection and observes high latency for online predictions. What can they do to reduce latency?

Refer to the exhibit. A team deploys a model with the above configuration. They observe that during traffic spikes, the endpoint does not scale up quickly enough, causing increased latency. The average CPU utilization never exceeds 50%. What is the most likely reason for the slow scaling?

You are fine-tuning a BERT model from Hugging Face Transformers on Vertex AI. You want to minimise cost for a short experiment. Which compute configuration should you use?

Your team has deployed a model on Vertex AI endpoints. You need to monitor the prediction latency to ensure it meets a 99th percentile SLO of 500ms. You want to set up an alert if the latency exceeds this threshold. Which metric should you use?

A team is building a CI/CD pipeline for ML using Cloud Build. The pipeline trains a model and deploys it to Vertex AI. Recently, a change in the data processing step caused the model to be trained with a different data version, leading to a failed deployment because the model was invalid. How should the team prevent this in the future?

A company serves a scikit-learn model on Vertex AI Prediction but receives a 400 error with 'Prediction failed: Model evaluation error'. What is the most likely cause?

A team is monitoring a production ML system that includes multiple models and data processing pipelines. They want to set up a comprehensive alerting strategy that minimizes false positives while ensuring critical issues are promptly addressed. Which approach is the most effective?

An ML engineer is creating a Vertex AI Pipeline that includes a loop to train multiple models in parallel on different hyperparameter sets. Which TWO KFP SDK v2 constructs can be used to implement this parallel execution?

A company is deploying multiple models on a single Vertex AI endpoint to reduce costs. Each model has different traffic patterns. Which configuration should they use?

A machine learning engineer is exporting a trained model from Vertex AI Training to the Model Registry. Which artifact should they upload as the model artifact?

A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?

A team wants to collect ground truth labels for their model deployed on Vertex AI Endpoint to perform model quality monitoring. They have a process that generates actual outcomes within 24 hours of prediction. What is the recommended approach for storing these labels?

A retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. Which approach should they take?

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Which THREE are key capabilities of Vertex AI Feature Store?

A Vertex AI pipeline is triggered from Cloud Build using the configuration above. The pipeline fails with an error: 'Unable to submit build: The source code is not available.' What is the most likely cause?

A data engineer wants to compute feature aggregates over a large dataset stored in BigQuery and write the results to Vertex AI Feature Store. The pipeline must handle both batch and streaming data. Which Google Cloud service should they use?

A team uses Vertex AI Pipelines for continuous training triggered by model drift. They want to monitor the pipeline execution cost and optimize resource usage. Which THREE metrics should they track? (Choose 3)

You are using Vertex AI to train a model with a custom container. You need to pass command-line arguments for hyperparameters. Which TWO methods can you use? (Choose 2.)

A team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?

A machine learning engineer is building a pipeline with Vertex AI Pipelines and wants to pass a large dataset between components without copying it to the container's memory. What is the best practice for passing data between pipeline components?

A company is fine-tuning a large language model (Gemma 7B) using Vertex AI JumpStart. They want to reduce the model's memory footprint for deployment on edge devices. Which THREE model compression techniques should they consider?

A company wants to deploy a TensorFlow model on edge devices for real-time inference without internet connectivity. Which Vertex AI service should they use to manage the deployment?

Refer to the exhibit. A Machine Learning Engineer attempts to deploy a model to a Vertex AI Endpoint for online predictions but receives an error. What is the most likely cause of this error?

A machine learning engineer notices that the Vertex AI Prediction endpoint's error rate has increased over the past week. The model was retrained with new data and redeployed. Which step should the engineer take first to diagnose the issue?

Which TWO practices help ensure reproducible ML experiments?

A team uses custom training and deploys a TensorFlow model using Vertex AI Endpoints. They set up Cloud Monitoring alerts for online prediction latency. However, they notice the latency metric shows a spike every hour, but the actual user experience is fine. What could be the cause?

A company is training a large neural network on Vertex AI and training jobs keep failing with 'Out of memory' errors. The VM uses a standard n1-standard-4 machine with 15 GB RAM. Which action should they take first?

A financial services firm deploys a binary classification model for fraud detection. The model's precision is 0.95 and recall is 0.60 on the test set. After deployment, the fraud rate in production is 0.5% compared to 5% in the test set. The model shows good calibration on the test set (Brier score 0.02) but poor calibration in production (Brier score 0.15). What is the most likely explanation for the calibration degradation?

An engineer is configuring Vertex AI Model Monitoring for a model deployed on an endpoint. They want to monitor feature skew using the training dataset as a baseline. The training dataset is large (10 TB). What is the most efficient way to provide the baseline distribution?

Which TWO options are recommended practices for managing model versions across teams in Google Cloud?

A retail company wants to build a customer churn prediction model using BigQuery ML. The data is stored in BigQuery tables and includes customer demographics, purchase history, and support interactions. The data scientist wants to experiment with different model types quickly without moving data to another environment. Which approach should they use?

You are building a Vertex AI pipeline using the KFP SDK v2. One component processes a large dataset and outputs a metrics artifact. You notice that the component is being cached even when the dataset changes, because the component code and image remain the same. How can you force the component to always re-execute when the dataset changes?

A team wants to monitor features in Vertex AI Feature Store for drift. Which TWO configurations are required?

A data scientist notices that the model's prediction latency has increased over the last week. They need to investigate the root cause by examining request and response logs for the Vertex AI Endpoint. What is the recommended way to capture these logs?

An ML engineer is trying to upload a TensorFlow model to Vertex AI using the gcloud command shown. The model was trained using TensorFlow 2.11 and saved with model.save('model/'). The engineer sees the error. What is the most likely cause?

An organization has multiple ML pipelines running on Vertex AI. They want to centralize monitoring and alerting for pipeline failures, including root cause analysis. Which combination of services should they use?

A team uses Vertex AI Feature Store to serve features for real-time predictions. They notice that feature values are frequently updated from multiple source systems, leading to inconsistencies. They need to ensure that feature values are consistent across all serving endpoints. What should they do?

A team is troubleshooting a Vertex AI Pipelines run that keeps failing at the model evaluation step. The pipeline includes steps: data preprocessing, training, evaluation, and deployment. Which THREE actions should they take to diagnose the issue?

You are using Vertex AI Matching Engine for similarity search. Your index has 10 million embeddings of 512 dimensions. The query latency requirement is under 10ms for 99th percentile. Which index type should you choose?

A team wants to implement continuous delivery for their ML models. They have a pipeline that trains a model and evaluates it. If the evaluation metrics exceed a threshold, the model should be deployed to a staging endpoint, and after manual approval, to production. Which approach should they use?

A team is building a continuous training pipeline that retrains a model when new data arrives. They want to detect data drift between the training dataset and the serving data. Which approach should they integrate into the pipeline to compare the distributions of the two datasets?

You are using Vertex AI Vector Search with an approximate nearest neighbor index. You need to update the index with new data every hour. The updates must be available for queries immediately. Which update method should you use?

A healthcare provider needs to extract structured information from incoming PDF forms (e.g., patient intake forms). They want to automate data extraction without writing custom models. Which Google Cloud service should they use?

Match each ML pipeline component to its description.

You are using Vertex AI Vector Search for a product recommendation system. Your index is updated with new embeddings every hour. To minimize query latency while keeping the index fresh, what should you do?

An engineer wants to configure alerting when the data distribution of a serving feature deviates from the training data distribution. The model is deployed on Vertex AI Endpoints. Which divergence metric should they use to compare the training and serving distributions?

A data scientist is fine-tuning a large language model from Hugging Face using Vertex AI Training with a GPU. The model has 7 billion parameters and does not fit on a single GPU. They need to split the model across multiple GPUs and train with data parallelism. Which strategy should they use?

You are designing a Vertex AI pipeline that includes a container component. The component needs to use a custom container image that is stored in Artifact Registry. How should you specify the container image in the component definition?

A data scientist wants to share a trained model with colleagues for evaluation. The model is stored as a Vertex AI Model resource. What is the recommended way to share the model without exposing the underlying project?

Refer to the exhibit. A team deploys a model using Cloud Run. They notice that after scaling up, the new instances take about 90 seconds to become ready and serve requests. They want to reduce this startup time. Which configuration change is most likely to help?

A retail company wants to generate product recommendations on their website using Google Cloud. They have historical transaction data and need a managed service that provides personalized recommendations like 'frequently bought together'. Which service should they use?

An organisation uses Delta Lake on Dataproc to manage a data lake for ML training. They need ACID transactions for concurrent reads and writes. Which file format does Delta Lake use as the underlying storage?

An e-commerce company uses a recommendation model deployed on Vertex AI Endpoints. The model's latency increases gradually over two weeks, causing timeouts. The model is served using a custom container. What is the most likely root cause and corrective action?

A team is deploying a model that has strict latency requirements: p99 response time under 100 ms. The model is CPU-only and will receive up to 1000 QPS. They want to minimize cost while meeting the SLO. Which machine type and scaling configuration is most appropriate?

Match each Google Cloud storage option to its best use case.

A team wants to enforce governance and compliance for all ML models across the organisation. They need a centralised repository that tracks model versions, deployment history, and evaluation metrics. Which service should they use?

You are fine-tuning a large language model (LLM) from Vertex AI Model Garden using a custom dataset. You need to minimize training cost while maintaining reasonable throughput. Which THREE strategies should you combine?

You are building a machine learning pipeline on Google Cloud. You need to perform feature engineering on large datasets stored in BigQuery and store the resulting features in Vertex AI Feature Store for both online and offline use. Which TWO Google Cloud services should you use?

You need to run batch predictions on a large dataset stored in BigQuery using a Vertex AI model. The dataset contains 10 million rows, and each prediction takes about 100ms. You want to minimize cost and execution time. What should you do?

An MLOps engineer is setting up monitoring for a deployed model on Vertex AI Endpoints. Which TWO actions are required to enable Vertex AI Model Monitoring for feature skew and drift? (Choose two.)

A data science team is deploying a large NLP model to Vertex AI for real-time inference. They notice high latency per request. Which action should they take first to reduce latency?

A company is using Vertex AI Pipelines with reusable components. They observe that a component that performs hyperparameter tuning is failing intermittently with a 'ResourceExhausted' error. The component is configured with a small custom service account. What is the most likely cause?

A company uses Vertex AI Pipelines to train and deploy models. They want to automatically generate model documentation that includes model details, intended use, and evaluation results. What should they use?

Refer to the exhibit. A data scientist is evaluating a binary classification model trained with BigQuery ML on an imbalanced dataset. The exhibit shows the output of ML.EVALUATE run on two different thresholds. Which TWO actions should the data scientist take to improve model performance? (Choose two.)

A company uses Cloud Composer to orchestrate a nightly ML workflow that includes running a Vertex AI pipeline, querying BigQuery, and running a Dataflow job. The Airflow DAG must run only if the previous day's Dataflow job succeeded. Which Airflow concept should they use to implement this dependency?

A team uses Vertex AI Prediction with a custom container. They want to perform canary deployments by sending 5% of traffic to a new model version. Which method should they use?

An ML engineer is scaling a prototype to production using Vertex AI Pipelines. The pipeline includes data validation, preprocessing, training, and deployment steps. They want to ensure that the pipeline can be reproduced and audited. What is the best practice?

A data scientist wants to define a lightweight Python function component in Vertex AI Pipelines using Kubeflow Pipelines SDK v2. Which decorator should be applied to the function to make it a pipeline component?

Refer to the exhibit. A data scientist trained a BigQuery ML classification model to detect fraudulent transactions. The dataset has 95% non-fraud (class 0) and 5% fraud (class 1). The evaluation metrics show high accuracy (0.91) but low recall (0.60) for fraud detection. Which low-code approach should the data scientist take to improve recall without significantly sacrificing precision?

A large financial company uses a complex ML pipeline to detect fraudulent transactions. The pipeline consists of multiple steps: data ingestion from Pub/Sub, feature engineering using Dataflow, model training with Vertex AI, and deployment to an endpoint. They currently use Cloud Composer to orchestrate the pipeline with separate DAGs for each step. Recently, they have been experiencing failures in the Dataflow job due to schema changes in the incoming transactions, causing the pipeline to stall. The team manually fixes the schema and re-runs the pipeline, which is time-consuming. They want to improve the robustness of the pipeline. The pipeline is run on a schedule but also triggered by the arrival of new data. The team is considering moving to Vertex AI Pipelines to unify the workflow. They also want to automatically detect schema changes and handle them without manual intervention. Which approach should they take?

You have a Vertex AI endpoint serving a model with min replicas=2 and max replicas=10. You notice that during low traffic hours, the endpoint still runs 2 replicas, incurring costs. You want to reduce costs to zero when there is no traffic. What should you do?

An ML team is fine-tuning a large language model using a custom container on Vertex AI. They want to reduce costs by using preemptible (spot) VMs for training. The training job is long-running and uses checkpointing. Which statement is correct regarding spot VM usage?

A company wants to use pre-built Google Cloud APIs for text analysis. Which TWO APIs can they use? (Choose TWO.)

A company is experiencing high prediction costs on Vertex AI Endpoints. They want to monitor and optimize costs. Which THREE actions should they take? (Choose 3)

An MLOps engineer is configuring Vertex AI Model Monitoring for a deployed model. They want to monitor for feature skew between training and serving data, but only for a subset of features. The training data has 100 features, and they want to monitor only the top 10 most important features to reduce cost and noise. How can they achieve this?

A healthcare startup is building a diagnostic tool that uses a deep learning model to classify medical images. The model is trained on TensorFlow and deployed on Vertex AI Prediction. The startup has strict latency requirements: predictions must return within 200 ms for 95% of requests. Current performance shows p95 latency of 350 ms. The team has already tried using a smaller model, but accuracy dropped below acceptable levels. The traffic pattern is spiky: low load during nights but bursts of 1000 requests per second during business hours. Currently, they use a single n1-highmem-8 VM with a GPU attached. They have a budget for additional resources but need to optimize cost. The model is about 500 MB and requires GPU for inference. Which course of action should they take to meet the latency requirement while managing costs?

A company needs to build a custom model to classify images of products into categories. They have a large labeled dataset. They want to use AutoML but are unsure which options support image classification. Which TWO AutoML products support image classification?

A machine learning engineer is training a TensorFlow model on Vertex AI using distributed training with the MultiWorkerMirroredStrategy. The training job uses 4 workers with 4 GPUs each. The engineer notices that the training is not scaling linearly. What is the most likely cause?

A team is using Vertex AI Pipelines to deploy a model. They have a component that evaluates the model and produces a ClassificationMetrics artifact. The pipeline should deploy the model only if the precision is greater than 0.9. They use dsl.If to check the metric. However, the condition always evaluates to False. What is the most likely cause?

A research team is training a very large Transformer model that does not fit into the memory of a single GPU. They have access to multiple GPUs on a single machine and want to split the model layers across GPUs. Which distributed training strategy should they use?

A company uses Vertex AI Pipelines to train models on a daily schedule. The pipeline includes a component that runs a BigQuery query to extract features. The team wants to ensure that if the BigQuery component fails due to transient network errors, the pipeline automatically retries it. How can they configure retries in Vertex AI Pipelines?

Which TWO of the following are benefits of using BigQuery ML for low-code model development?

An ML team wants to deploy multiple models (e.g., a recommender and a classifier) behind a single Vertex AI endpoint. The models have different resource requirements: the recommender needs GPU, the classifier needs high memory. How should they configure the endpoint?

Which API is recommended for high-throughput, low-latency online prediction requests to Vertex AI endpoints?

A model serving team is experiencing high latency in production. Which TWO actions should they take to diagnose the root cause? (Choose 2.)

You are designing a distributed training job for a PyTorch model on Vertex AI using multiple machines with GPUs. Which TWO configurations are required to enable data parallelism with PyTorch DDP? (Choose 2.)

A pipeline includes a component that produces a model artifact. The team wants to automatically detect skew between the training data distribution and the serving data distribution. Which three best practices should they implement? (Choose three.)

A company wants to log all prediction requests and responses from a Vertex AI Endpoint to BigQuery for auditing and debugging. How can they achieve this?

An ML engineer has deployed a model on Vertex AI Endpoints and wants to detect when the serving data distribution differs from the training data distribution. Which monitoring feature should they enable?

You have a Vertex AI pipeline that trains a model and outputs a Model artifact. You want to register this model in the Vertex AI Model Registry. Which pre-built Google Cloud Pipeline Components component should you use?

A machine learning engineer needs to schedule a Vertex AI pipeline to run daily at midnight. Which approach should they use?

A model deployed on Vertex AI Prediction is returning high latency for real-time requests. The model is a small TensorFlow model. Which troubleshooting step should the team take first?

A financial services company uses a custom container to serve a fraud detection model on Vertex AI Endpoints. The model requires a feature store lookup for each prediction. Recently, the feature store (Cloud Bigtable) experienced a brief outage, causing some predictions to fail. After the outage resolved, the endpoint's CPU utilization dropped significantly, and prediction latency improved. However, the model's false positive rate increased sharply. The ML engineer suspects the model is using stale features because the feature store outage caused missing lookups. Cloud Monitoring for the endpoint shows no errors after the outage, but the number of feature store read requests per prediction decreased by 30%. Which metric should the engineer use to confirm the hypothesis of stale features?

A data science team has trained a TensorFlow model on-premises using a large dataset. When they try to deploy the model to Vertex AI for online predictions, the deployed model fails to start with a ‘MemoryError’. The model artifact is 2 GB, and the machine type is n1-standard-4 (15 GB RAM). What is the most likely cause?

A data science team uses Vertex AI Model Monitoring to detect data quality issues in a production model. Which TWO metrics should they enable to identify problems with missing values in predictions? (Select TWO.)

An ML engineer notices that predictions are taking longer than expected under moderate traffic. Reviewing the endpoint configuration, what is the most likely cause of the high latency?

An ML pipeline runs on Vertex AI and includes a component that uses a third-party library not available in the default Python environment. The team wants to avoid building a custom container image. Which approach should they use?

A company needs to extract text from scanned invoices and parse key fields like invoice number and total amount. Which Document AI processor should they use?

You have a Vertex AI endpoint serving a model for real-time predictions. The endpoint is configured with minReplicaCount=2 and maxReplicaCount=10. Over the past week, you notice that the actual number of replicas rarely exceeds 2, but the average CPU utilization is around 85%. You want to reduce costs without impacting performance. What should you do?

An organization wants to deploy a TensorFlow model on edge devices such as smartphones and IoT devices for offline inference. Which format should they export the model to?

A company has deployed a machine learning model that uses a large input tensor. They notice that the prediction latency varies significantly between requests of the same size. Cloud Monitoring shows that the serving endpoint's CPU utilization is consistently below 50%, but memory utilization fluctuates between 70% and 95%. What is the most likely cause?

You need to deploy a model to a Vertex AI endpoint that can scale down to zero when there are no requests to minimize costs. Which feature should you enable?

A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?

For a low-latency real-time serving requirement, which type of Vertex AI Endpoint is appropriate?

You need to run a distributed training job on Vertex AI using TensorFlow with MirroredStrategy on a single machine with 4 GPUs. Which training configuration should you use?

A team uses Vertex AI Feature Store with an online store. They need low-latency serving for millions of features with high write throughput. Which online store type should they choose?

A company uses Vertex AI Model Monitoring. Which two configuration options can be set to reduce false positive drift alerts?

Match each MLOps practice to its description.

You need to query a Vertex AI Vector Search index for nearest neighbours. The index is deployed on an endpoint. Which API method should you use to perform the query?

An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)

A team is building a CI/CD pipeline for an ML model. They want to automatically trigger a Vertex AI pipeline for retraining whenever new training data arrives in a Cloud Storage bucket, but only if a specific Pub/Sub notification is published by a data ingestion process. Which approach meets these requirements with minimal operational overhead?

A media company wants to transcribe audio files from customer support calls into text for analysis. The audio is in English with clear speech and no background noise. They want a quick solution with no ML model training. Which Google Cloud service should they use?

Which TWO actions are recommended to detect and mitigate data drift in a production ML system on Vertex AI?

A data science team is building a feature engineering pipeline that processes large-scale data from BigQuery daily. They need to compute aggregate features and store the results in Vertex AI Feature Store for both online serving and offline training. Which Google Cloud service is best suited for this batch computation?

An ML engineer is using Vertex AI Pipelines and wants to reuse a trained model across multiple pipeline runs without retraining each time. Which artifact management strategy should be used?

You are using tf.Transform to preprocess data for a TensorFlow model. You want to ensure that the same transformations applied during training are also applied during serving. Which THREE components are necessary to achieve this?

A data analyst wants to use BigQuery ML to train a linear regression model (LINEAR_REG) to predict house prices. They have a table with features like square footage, number of bedrooms, and location. Which TWO statements about the training process are correct?

A team is using Vertex AI Explainability with a deployed model. They need to generate explanations for image classification predictions. Which explanation method should they configure in the ExplanationSpec?

A data engineer is troubleshooting a Vertex AI Endpoint that serves a large BERT model. After deployment, many prediction requests fail with 'Out of Memory' errors. The machine type is n1-standard-8 (30 GB memory) with no accelerator. Which action will most likely resolve the issue?

A data scientist deployed a classification model on Vertex AI Endpoints. After a week, the model's accuracy drops significantly from 92% to 78%. The data scientist suspects training-serving skew. What is the first step to confirm this?

A team is monitoring a batch prediction job on Vertex AI. Which two metrics should they monitor to ensure the job completes successfully without errors?

An ML engineer needs to monitor the error rate of prediction jobs on a Vertex AI Endpoint. Where can they view the number of failed prediction requests over time?

Your PyTorch training script uses DistributedDataParallel (DDP) across 4 vertices each with 4 GPUs (16 GPUs total). You submit a Vertex AI custom training job. How should you configure the worker pool spec?

An ML team is moving from a prototype Jupyter notebook to a production training pipeline. They want to ensure reproducibility. Which approach should they take?

You are designing an ML pipeline for a large-scale recommendation system that runs weekly retraining on historical user interaction data. The pipeline uses TensorFlow and is deployed on Google Cloud. The pipeline must be orchestrated and automated with minimal manual intervention. Which THREE options should you include in your design? (Choose three.)

Refer to the exhibit. A user is trying to upload a Vertex AI pipeline definition. The error indicates an invalid dependency order. What should the user do to fix this?

A company wants to deploy a model for real-time inference with high availability across multiple Google Cloud regions. The model is small and stateless. Which two steps should they take? (Choose two.)

A team wants to ensure that only approved models are deployed to production. Which Vertex AI feature should they use?

A data scientist trains an XGBoost model on Vertex AI with a custom container. The model performs well on a held-out test set but fails to generalize in production. They suspect data leakage between training and validation. What is the best practice to prevent this?

A team wants to implement automated model documentation that captures training data, feature importance, evaluation metrics, and intended use. Which Vertex AI feature supports this?

A model deployed on Vertex AI Prediction repeatedly exits with code 137. What is the most likely cause?

A data scientist trained a model on a single GPU but needs to train on multiple GPUs for a larger dataset. They observe that training time does not decrease linearly with additional GPUs. Which common issue is most likely?

A team is using Cloud Composer to orchestrate ML workflows. They have a DAG that triggers a Vertex AI Training job, then a prediction deployment. The deployment step occasionally fails due to quota limits. What is the best way to handle this?

A team deploys a model using Vertex AI Endpoint with automatic scaling. They observe that during traffic spikes, new instances take a long time to become ready, causing high latency for some requests. What should they configure to reduce this startup time?

An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?

A retail company wants to predict customer churn using their transaction history and customer demographics. They have limited ML expertise and want to use a managed service on Google Cloud. Which service should they use?

A machine learning engineer is preparing to train a Transformer-based model using TensorFlow on a single TPU v3-8 pod slice. The training script uses tf.distribute.TPUStrategy. Which environment variable must be set in Vertex AI to enable TPU training with the appropriate topology?

A financial services company has deployed a credit risk ML model on Vertex AI. They want to monitor the model for fairness across demographic groups to ensure no biased outcomes. Which TWO actions should they take as best practices? (Choose TWO.)

A company uses BigQuery to store feature data for ML training. A data engineer notices that a Vertex AI Training job is failing with 'Access Denied' errors when reading from a BigQuery table. The training job uses a custom service account that has been granted the 'bigquery.dataViewer' role on the dataset. What is the most likely cause of the failure?

Which TWO statements are true about canary deployments for Vertex AI endpoints?

A data scientist wants to train a TensorFlow model on Vertex AI using a pre-built container. Which of the following pre-built containers is NOT available for custom training in Vertex AI?

You are using TensorFlow Transform (tf.Transform) to preprocess data for a model that will be deployed on Vertex AI. What is the primary benefit of using tf.Transform over Dataflow alone?

A financial services company deploys a fraud detection model on Vertex AI. The model must make predictions in under 100ms. After deployment, latency spikes to 300ms during peak hours. The model is a large ensemble with 500MB size. Which action is most likely to reduce latency?

An ML engineer wants to monitor the latency of online predictions from a Vertex AI Endpoint. They need to track p50, p95, and p99 latency over time and set up alerts if p99 exceeds 1 second. Which approach should they take?

A machine learning engineer wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they use?

You are the ML engineer for a financial services company. You have deployed a fraud detection model on Vertex AI Endpoints using a custom container. The model is a gradient boosting model trained on transactional data. Over the past week, the model's precision has dropped from 95% to 80%, while recall has remained stable. The input data volume and distribution have not changed significantly. The model is served on a single endpoint with autoscaling enabled (min replicas=2, max replicas=10). You notice that the average CPU utilization of the serving containers has increased from 40% to 90%, and the p99 latency has increased from 50ms to 200ms. The model is retrained weekly using the latest data, and the last retraining was 3 days ago. The logs show no errors, and the model version is unchanged. Given these symptoms, what is the most likely cause of the precision drop?