PMLE Practice Exam 61 — 20 Questions

Question 1

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

Accepted Answer

Use Vertex AI Prediction with multi-model serving by deploying multiple models to one endpoint with traffic splits.. Vertex AI Prediction supports multi-model serving, allowing you to deploy multiple models to a single endpoint and use traffic splits to route a percentage of requests to each model. This reduces costs by sharing underlying infrastructure (e.g., compute resources) across models, rather than provisioning separate endpoints or containers for each model.

Answer

Use Cloud Run to serve each model separately.

Answer

Package all models into a single container and deploy that container.

Answer

Deploy each model to its own endpoint and use a load balancer.

Question 2

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?

Accepted Answer

Set the max_retry_count in the worker pool spec to a value greater than 0. To handle spot VM preemptions, the training job must be restartable. Using checkpoints allows the job to resume from the last saved state. Vertex AI automatically retries on preemption if the job is restartable (managed by the service). Setting max_retry_count in the worker pool spec allows Vertex AI to automatically restart the job after preemption. Also, reducing machine type or increasing parallel trials are not direct solutions.

Answer

Use a larger machine type to reduce training time

Answer

Increase the number of parallel trials in hyperparameter tuning

Answer

Set the worker_pool_specs to use spot VMs by setting spot=True

Question 3

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?

Accepted Answer

Use Vertex AI Model Evaluation with sliced evaluation in BigQuery. Vertex AI Model Evaluation supports sliced evaluation, allowing comparison of metrics (like accuracy, precision) across subgroups defined by features like age, gender, etc.

Answer

Use Vertex AI Explainability to compute feature attributions per subgroup

Answer

Use Cloud Monitoring custom metrics to track predictions per subgroup

Answer

Use Vertex AI Model Monitoring to detect skew in demographic features

Question 4

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?

Accepted Answer

Implement a retraining pipeline that periodically retrains the model on recent data.. Gradual data distribution shifts (concept drift) require the model to adapt to new patterns over time. A retraining pipeline that periodically retrains on recent data ensures the model remains aligned with the current production distribution, directly addressing the degradation caused by drift without relying on static historical data.

Answer

Increase the regularization strength to prevent overfitting.

Answer

Increase the amount of training data by using more historical records.

Answer

Switch to a more complex model architecture to better capture patterns.

Question 5

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?

Accepted Answer

LINEAR_REG. The question specifies a linear regression model for predicting house prices, which is a regression task with a continuous target variable. BigQuery ML's LINEAR_REG model type is explicitly designed for linear regression, making it the correct choice for this use case.

Answer

BOOSTED_TREE_REGRESSOR

Answer

LOGISTIC_REG

Answer

DNN_REGRESSOR

Question 6

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?

Accepted Answer

The model is automatically evaluated on a held-out test set if data splitting is enabled. When data splitting is enabled in BigQuery ML, the `CREATE MODEL` statement automatically reserves a portion of the input data as a held-out test set. After training completes, BigQuery ML evaluates the model on this test set and reports metrics like mean absolute error and R², without requiring any manual split or separate evaluation step.

Answer

The analyst must call ML.TRAIN after CREATE MODEL to start training

Answer

The trained model is stored in Cloud Storage

Answer

The model must be exported to Vertex AI for prediction

Question 7

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)

Accepted Answer

Change Pub/Sub to a push subscription that sends messages to a load-balanced group of VMs. Switching to a push subscription with a load-balanced group of VMs distributes the message processing load across multiple instances, preventing any single VM from being overwhelmed. This directly addresses the memory exhaustion issue by parallelizing the work and allowing horizontal scaling.

Answer

Deploy the model on Vertex AI Prediction for batch prediction

Answer

Switch to a larger VM with more memory

Answer

Store results in Cloud SQL instead of BigQuery

Question 8

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?

Accepted Answer

Enable Vertex AI Model Monitoring to track prediction drift and alert when metrics exceed thresholds.. Vertex AI Model Monitoring tracks prediction drift and alerts when metrics exceed thresholds, enabling proactive detection of model degradation. Option B is wrong because Cloud Logging captures requests and responses but does not automatically detect drift, requiring manual review. Option C is wrong because shuffling training data does not help detect drift. Option D is wrong because scheduling retraining without monitoring cannot proactively detect issues before they cause loss.

Answer

Set up Cloud Logging to capture all prediction requests and responses for manual review.

Answer

Randomly shuffle the training data before retraining to improve robustness.

Answer

Schedule a monthly job to retrain the model with the latest data without monitoring.

Question 9

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?

Accepted Answer

OpenCensus or OpenTelemetry SDK. OpenCensus and OpenTelemetry are the recommended open-source frameworks for exporting custom metrics from custom containers on Vertex AI Prediction to Cloud Monitoring. They provide a standardized way to instrument your application code, collect metrics, and send them directly to Cloud Monitoring via the Cloud Monitoring API, without requiring additional agents or log-based workarounds.

Answer

Vertex AI built-in metrics

Answer

Stackdriver Monitoring agent installed in the container

Answer

Cloud Logging log-based metrics

Question 10

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?

Accepted Answer

Deploy both versions on the same endpoint and use the `traffic_split` parameter to allocate 90% to v1 and 10% to v2.. Vertex AI Endpoints allow distributing traffic between deployed models using the traffic_split parameter. Setting 90% to v1 and 10% to v2 achieves the A/B test traffic split. Option B is incorrect because a global load balancer in front of two endpoints adds unnecessary complexity and is not the native Vertex AI method. Option C is incorrect because the client randomly selecting endpoints introduces client-side logic and does not leverage Vertex AI's built-in traffic splitting. Option D is incorrect because Cloud Deployment Manager's canary rollout is for infrastructure deployment, not for model traffic splitting; Vertex AI provides traffic splitting for A/B testing.

Answer

Configure a global load balancer in front of two endpoints and set the weight.

Answer

Create two separate endpoints, one for each version, and have the client randomly select the endpoint.

Answer

Deploy v2 as a canary deployment and set the canary rollout to 10% in Cloud Deployment Manager.

Question 11

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?

Accepted Answer

Vertex AI JumpStart. Vertex AI JumpStart provides one-click deployment and fine-tuning of foundation models, including BERT and other NLP models. Model Garden is for model discovery, but fine-tuning is typically done via JumpStart. AutoML is for training custom models, not fine-tuning existing ones. Custom training requires more manual effort.

Answer

Vertex AI Model Garden

Answer

Vertex AI AutoML

Answer

Vertex AI Custom Training

Question 12

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.)

Accepted Answer

dsl.Else. In Kubeflow Pipelines SDK v2, `dsl.If` and `dsl.Else` are the constructs used to create conditional execution branches within a pipeline. `dsl.If` evaluates a condition (e.g., model accuracy > 0.9) and runs the enclosed steps only if true; `dsl.Else` defines the alternative branch that runs when the condition is false. This directly implements the required logic for running additional evaluation on high accuracy or data augmentation otherwise.

Answer

dsl.ParallelFor

Answer

dsl.ExitHandler

Answer

dsl.Collected

Question 13

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?

Accepted Answer

Increase the user exploration parameter in the Vertex AI Recommendations configuration. Increasing the user exploration parameter in Vertex AI Recommendations instructs the model to allocate a higher percentage of recommendations to items with less historical data, effectively enabling personalized suggestions for cold-start users based on available demographic signals. This parameter directly controls the balance between exploiting known user-item interactions and exploring new or less-seen items, which is the standard mechanism within Vertex AI's built-in collaborative filtering to address the cold-start problem without requiring a custom model.

Answer

Collect more historical interaction data before showing recommendations

Answer

Disable recommendations for new users until they have at least 10 interactions

Answer

Build a custom two-tower recommendation model using Vertex AI Training

Question 14

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?

Accepted Answer

The user does not have the 'roles/aiplatform.admin' or the 'aiplatform.models.upload' permission on the project.. The error message indicates a permission issue during model upload. The user lacks the 'aiplatform.models.upload' permission or the broader 'roles/aiplatform.admin' role on the project. Vertex AI requires these IAM permissions to authorize the upload action, regardless of other resource accessibility.

Answer

The container image 'gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-12:latest' is not accessible.

Answer

The user specified an incorrect region (us-central1) that does not support Vertex AI.

Answer

The Cloud Storage bucket 'gs://my-model-artifacts/fraud-detection/v2/' does not exist.

Question 15

Match each ML model interpretability method to its description.

Accepted Answer

LIME: Local interpretable model-agnostic explanations by approximating locally with a simpler model.. LIME (Local Interpretable Model-agnostic Explanations) provides local explanations by approximating the model's behavior near a specific prediction using a simpler interpretable model. SHAP (SHapley Additive exPlanations) uses Shapley values from game theory to fairly distribute feature contributions for individual predictions. Partial Dependence Plots (PDP) show the average marginal effect of one or two features on the predicted outcome across the dataset, making it a global method. Permutation Feature Importance measures the increase in prediction error when a feature's values are randomly shuffled, indicating feature importance globally. Common mistakes: confusing LIME with a global method (B) and misattributing SHAP to visualizing decision trees (D).

Answer

LIME: Global interpretability method that provides feature importance rankings across entire dataset.

Answer

SHAP: A method for visualizing individual decision trees.

Question 16

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?

Accepted Answer

Use the Airflow Vertex AI pipeline operator with wait_for_completion=True.. Vertex AI pipelines are asynchronous; the Airflow operator should wait for completion using a sensor or by setting the wait_for_completion parameter. Increasing the task timeout is a workaround but not best practice. Using a separate DAG for the pipeline defeats the purpose of orchestration.

Answer

Increase the Airflow task timeout to account for the maximum expected pipeline duration.

Answer

Run the pipeline synchronously by using a Kubernetes Pod operator.

Answer

Split the DAG into two DAGs: one for the pipeline, one for the rest.

Question 17

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

Accepted Answer

Cloud Natural Language API. Cloud Natural Language API is a low-code ML service that provides pre-trained models for analyzing text, including sentiment analysis, entity recognition, and syntax analysis, without requiring custom model training. It is appropriate for a data analyst who wants to quickly extract insights from text data using simple API calls.

Answer

Vertex AI Workbench

Answer

Document AI

Answer

BigQuery ML for sentiment

Question 18

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?

Accepted Answer

Create a Cloud Scheduler job that calls the Vertex AI API to create a pipeline job.. Cloud Scheduler is the native Google Cloud service for cron-based job scheduling. By configuring a Cloud Scheduler job to call the Vertex AI API (e.g., via a HTTP POST to the projects.locations.pipelineJobs.create endpoint), the engineer can trigger a pipeline run every 24 hours. This approach is reliable, supports authentication via OAuth, and integrates directly with Vertex AI Pipelines without requiring additional orchestration code.

Answer

Use Cloud Tasks to queue pipeline runs daily.

Answer

Set a cron expression in the pipeline definition file using the 'schedule' parameter.

Answer

Use the Vertex AI Pipelines UI to set a schedule directly on the pipeline.

Question 19

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?

Accepted Answer

Set up a Cloud Build trigger that runs on push to main branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.. Cloud Build triggers can be configured to fire specifically on pushes to the main branch. The build step then uses the gcloud command to submit a Vertex AI Pipeline job, which directly integrates the CI/CD pipeline with Vertex AI's orchestration. This approach is event-driven, immediate, and requires no additional services or polling.

Answer

Set up a Cloud Build trigger that runs on push to any branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.

Answer

Use a Cloud Scheduler job to periodically check for new commits on main and trigger Cloud Build.

Answer

Use Cloud Functions to watch the repository and call Cloud Build on push to main.

Question 20

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)

Accepted Answer

GPU utilization on the endpoint. CPU/GPU utilization can indicate resource exhaustion causing errors. Prediction job failures metric directly shows failed predictions.

Answer

Feature drift alert count

Answer

Vertex AI Model Monitoring skew score

Answer

Number of predictions per minute