PMLE Practice Exam 2 — 20 Questions

Question 1

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?

Accepted Answer

Check for data drift using Vertex AI Model Monitoring. A sudden drop in precision indicates that the model's predictions are no longer aligning with the ground truth, which is a classic symptom of data drift. Vertex AI Model Monitoring can automatically detect drift in feature distributions or prediction output compared to a baseline, allowing you to identify the root cause before taking corrective action. Retraining or reverting without first diagnosing the drift could waste resources or mask the underlying issue.

Answer

Retrain the model with more data

Answer

Increase the number of prediction nodes

Answer

Revert to the previous model version

Question 2

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?

Accepted Answer

Bayesian optimization (Vizier default). Bayesian optimization (the default algorithm in Vertex AI Vizier) is the correct choice because it explicitly balances exploration and exploitation by building a probabilistic model of the objective function and using an acquisition function to select the next hyperparameter configuration. With a budget of 50 trials, this algorithm efficiently converges to optimal regions while still exploring uncertain areas, making it ideal for tuning custom training jobs where each trial is computationally expensive.

Answer

Random search

Answer

Grid search

Answer

Manual search

Question 3

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)

Accepted Answer

Maintain two separate indexes: one for building and one for serving, and swap them after building.. Maintaining two separate indexes allows you to build a new index in the background while the existing index continues to serve queries. Once the new index is fully built and validated, you can swap it into production with minimal downtime. This is a common pattern for high-availability systems where index rebuilds are necessary.

Answer

Use batch updates to rebuild the entire index daily.

Answer

Increase the number of replicas on the deployed index.

Answer

Use a GPU-powered endpoint for faster indexing.

Question 4

Match each MLOps practice to its description.

Accepted Answer

CI/CD: Automates the processes of building, testing, and deploying ML models.. This matching question requires associating each MLOps practice (CI/CD, Model Monitoring, Data Versioning) with its correct description. The correct matches are: CI/CD automates building, testing, and deploying ML models (option A); Model Monitoring continuously observes model performance and detects data drift (option C); Data Versioning manages and tracks changes to datasets over time (option E). Common mistakes include confusing CI/CD with monitoring or versioning, or assigning model monitoring to data versioning tasks. The wrong options (B, D, F) are incorrect because they swap responsibilities: B wrongly attributes monitoring to CI/CD, D assigns versioning to monitoring, and F assigns CI/CD to versioning.

Answer

CI/CD: Continuously observes model performance and detects data drift.

Answer

Model Monitoring: Manages and tracks changes to datasets over time.

Answer

Data Versioning: Automates the processes of building, testing, and deploying ML models.

Question 5

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?

Accepted Answer

n1-standard-4 with 1 NVIDIA Tesla T4. The n1-standard-4 machine type supports attaching GPUs such as the NVIDIA Tesla T4, which provides GPU acceleration for low-latency online predictions. Vertex AI endpoints require a machine type that allows GPU attachment, and the n1-series is one of the few families that supports GPUs, while the T4 offers a good balance of cost and performance for inference workloads.

Answer

n1-standard-4

Answer

e2-standard-4

Answer

n1-highmem-8

Question 6

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?

Accepted Answer

Use spot VM instances for training. Spot VM instances are significantly cheaper than on-demand instances, reducing training cost. They can be preempted, but for fine-tuning tasks that can checkpoint and resume, this trade-off is acceptable for cost savings.

Answer

Use full fine-tuning of all model parameters

Answer

Use TPU v4 pods for training

Question 7

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?

Accepted Answer

projects.locations.indexEndpoints.findNeighbors. The correct API method to query a deployed Vertex AI Vector Search index for nearest neighbors is `projects.locations.indexEndpoints.findNeighbors`. This method is specifically designed for vector similarity search against an index endpoint, returning the nearest neighbors for a given query vector. The other options either target the wrong resource (indexes instead of indexEndpoints) or use methods intended for different purposes like model prediction.

Answer

projects.locations.indexes.match

Answer

projects.locations.indexes.query

Answer

projects.locations.endpoints.predict

Question 8

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?

Accepted Answer

Write the data to Cloud Storage and pass the GCS URI as an artifact.. In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.

Answer

Use the component's temporary directory to share data between containers.

Answer

Pass the data as a serialized Python object in memory.

Answer

Store the data in a BigQuery table and pass the table reference.

Question 9

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

Accepted Answer

ML.EVALUATE. ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.

Answer

ML.PREDICT

Answer

ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN

Question 10

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?

Accepted Answer

Use class weighting or synthetic oversampling (SMOTE) during training. Class weights or resampling techniques like SMOTE are standard for imbalanced datasets. Option A is correct. Option B (undersampling majority) can lose information. Option C (collect more data) is impractical. Option D (no alterations) will bias the model.

Answer

Randomly undersample the majority class to balance the dataset

Answer

Collect more data until the fraud rate increases

Answer

Train without any modifications; the model will naturally handle it

Question 11

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?

Accepted Answer

AutoML Natural Language. AutoML Natural Language is the correct choice because it allows the business to train a custom sentiment analysis model using their own labeled dataset without writing any code. It provides a low-code interface for uploading data, training, and deploying the model, which aligns with the requirement of no coding and a small labeled dataset.

Answer

Natural Language API

Answer

Vertex AI custom training with PyTorch

Answer

BigQuery ML with logistic regression

Question 12

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?

Accepted Answer

Add the labeler as a participant in the labeling task and assign IAM roles on the dataset. Labeling tasks are shared by granting the labeler role on the dataset resource. Option A is wrong because sharing the entire project gives too much access. Option B is wrong because the Data Labeling Service does not use Cloud Storage ACLs for task access. Option C is wrong because exporting and reimporting causes duplication.

Answer

Add the labeler's account as a Project Editor on the project

Answer

Share the Cloud Storage bucket containing the data with the labeler

Answer

Export the dataset and have the labeler create a new dataset

Question 13

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?

Accepted Answer

Use TFRecord format and streaming reads.. Using TFRecord format with streaming reads allows efficient, scalable data loading from Cloud Storage, reducing memory pressure and improving I/O performance. Option A is wrong because increasing batch size to maximum can cause memory issues and may not improve throughput. Option C is wrong because storing all data in memory is not scalable. Option D is wrong because a single powerful VM still has limits and is not cost-effective for large datasets.

Answer

Increase the batch size to maximum.

Answer

Store all data in memory before training.

Answer

Use a single powerful VM with high memory.

Question 14

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

Accepted Answer

Natural Language API. The Natural Language API provides entity extraction as a pre-trained model. Vision API is for images, Translation API for translation, and Dialogflow for conversational agents.

Answer

Translation API

Answer

Dialogflow

Answer

Vision API

Question 15

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?

Accepted Answer

Set the 'includedFiles' field to 'training/**' in the trigger configuration.. Cloud Build triggers support an `includedFiles` field that specifies a glob pattern. When set to `training/**`, the trigger will only fire if the commit includes changes to files under the `training/` directory. This is the native, declarative way to filter triggers based on changed file paths without additional infrastructure.

Answer

Add a 'ignoreFiles' field with 'training/**' to the trigger.

Answer

Use a 'substitutions' field with a regex pattern to filter commits.

Answer

Configure a Cloud Function to check the commit diff and call Cloud Build API conditionally.

Question 16

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

Accepted Answer

Use a GPU accelerator for the deployed model.. Using a GPU accelerator speeds up inference, and batching requests reduces overhead per request. Minimizing replicas doesn't help latency; increasing CPU doesn't always help if GPU is better.

Answer

Increase the number of CPU vCPUs per machine.

Answer

Set min_replica_count to 0 to avoid idle instances.

Answer

Decrease the number of replicas to reduce resource contention.

Question 17

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

Accepted Answer

frequently-bought-together. The 'frequently-bought-together' model type in Google Cloud Recommendations AI is specifically designed to identify items that are commonly purchased in the same transaction, using co-occurrence analysis of historical purchase data. This directly matches the requirement to show items that are frequently bought together, leveraging association rule mining (e.g., Apriori algorithm) to generate recommendations.

Answer

recently-viewed

Answer

recommended-for-you

Answer

others-you-may-like

Question 18

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?

Accepted Answer

Deploy to Vertex AI online prediction using a prebuilt container for scikit-learn.. Vertex AI online prediction with custom containers is suitable for scikit-learn models. Vertex AI will host the container and scale. Using AI Platform or Cloud Functions with a 200 MB model might hit limits.

Answer

Use Cloud Run with a custom container.

Answer

Create a Kubernetes cluster on GKE and deploy the model there.

Answer

Export the model as a Cloud Function.

Question 19

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?

Accepted Answer

Use Eventarc to route the Pub/Sub notification to a Cloud Function that calls the Vertex AI pipeline creation API.. Eventarc can directly listen to a Pub/Sub topic and route matching messages to a Cloud Function, which then calls the Vertex AI pipeline creation API. This serverless approach triggers the pipeline only when the specific Pub/Sub notification is published, meeting the requirement with zero infrastructure to manage and no polling overhead.

Answer

Use Cloud Scheduler to run a job every hour that checks for new files in Cloud Storage and starts the pipeline if new files exist.

Answer

Configure a Cloud Build trigger that listens to the Pub/Sub topic and executes a build step that submits the pipeline run.

Answer

Create a Dataflow streaming pipeline that reads from Pub/Sub and triggers the Vertex AI pipeline via a custom sink.

Question 20

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?

Accepted Answer

Switch to a machine type with more memory, e.g., n1-highmem-8, and increase min_replica_count.. The 502 errors likely indicate the instances are overwhelmed or timing out. Increasing the machine type to a high-memory instance reduces memory pressure, and adding more replicas through a lower target scaling metric or higher min replicas provides capacity. Tuning batch size helps but is secondary. GPU may not help if the issue is memory.

Answer

Increase the target CPU utilization to 90% to allow more requests per instance.

Answer

Enable canary traffic splitting to reduce load on the main endpoint.

Answer

Reduce the model batch size from 32 to 1 to lower memory per request.