PMLE Practice Test 50 — 15 Questions

Question 1

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)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cloud Dataflow for stream processing and feature computation. Cloud Dataflow is correct because it provides unified stream and batch processing with exactly-once semantics, enabling low-latency feature computation from streaming data. It integrates natively with Vertex AI Feature Store for online serving, ensuring features are updated within the required 5-second SLA.

Answer

Cloud Storage for feature store

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Cloud Functions for feature transformation

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BigQuery for feature storage

Question 2

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.)

Accepted Answer

Minimum and maximum number of nodes (autoscaling). The minimum and maximum number of nodes in autoscaling directly control how many compute instances are provisioned to handle prediction requests. A higher minimum node count increases baseline cost and reduces cold-start latency, while a lower maximum can cause queuing and higher latency under load, directly impacting both metrics.

Answer

Size of training dataset

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Model architecture (e.g., number of layers)

Question 3

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?

Accepted Answer

Store all features in a Feature Store for consistency.. Storing all features in a Feature Store (Option B) ensures consistency between training and serving, preventing training-serving skew. Vertex AI Feature Store provides a centralized repository for feature values, enabling reuse, point-in-time lookups, and online serving with low latency, which is critical for production reliability.

Answer

Manually scale instances based on historical traffic patterns.

Answer

Use a single large instance to simplify management.

Question 4

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?

Accepted Answer

Use streaming updates to add new embeddings incrementally. Vertex AI Vector Search supports streaming updates, allowing new embeddings to be added incrementally without rebuilding the entire index. This ensures low latency for new items by making them searchable almost immediately after update, which is critical for hourly refresh cycles where batch rebuilds would introduce significant delay.

Answer

Batch rebuild the index every hour

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Create a new index each hour and swap endpoints

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Use brute-force index to simplify updates

Question 5

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?

Accepted Answer

Check if the machine type is too small and enable autoscaling. High latency for real-time predictions from a small TensorFlow model often indicates that the serving infrastructure is under-provisioned. Checking the machine type and enabling autoscaling directly addresses whether the instance is too small to handle the request volume, which is the most common first step in diagnosing latency issues on Vertex AI Prediction.

Answer

Retrain the model with a larger batch size

Answer

Use a custom container with optimized runtime

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Enable Cloud Armor to reduce traffic

Question 6

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?

Accepted Answer

Use Cloud Storage Pub/Sub notifications to send events to a Cloud Function that triggers the pipeline.. The best approach is to use Cloud Storage notifications via Pub/Sub, then a Cloud Function that receives the event and calls the Vertex AI API to create a pipeline job. This is a common event-driven pattern. Cloud Scheduler is for scheduled triggers, not event-driven. Cloud Tasks and Cloud Run are not typically used for this purpose.

Answer

Use Cloud Tasks to queue a pipeline run whenever a new file is uploaded.

Answer

Configure the pipeline to run on a schedule and check for new data inside the pipeline.

Answer

Set up a Cloud Scheduler job that runs every minute to check for new files.

Question 7

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?

Accepted Answer

Review the custom container's startup time and consider pre-warming or reducing model loading time.. The root cause is likely that the custom container takes a long time to start (model loading), preventing the endpoint from scaling quickly. Pre-warming or reducing model loading time addresses this directly. Option A (increasing max replicas) does not solve the scaling delay. Option C (changing to higher CPU machine) may help but does not address the scaling speed. Option D (increasing min replicas) would be costly and still not handle sudden spikes if new replicas start slowly.

Answer

Increase the maxReplicaCount to 20 to allow more instances during spikes.

Answer

Change the machine type to a higher CPU machine like n1-standard-8.

Answer

Set the minReplicaCount to 5 to ensure enough capacity at all times.

Question 8

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?

Accepted Answer

Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift.. The prediction drift alert indicates a shift in prediction distribution, but accuracy is stable. This suggests data drift (change in input features) rather than concept drift. The engineer should first investigate input feature distributions to confirm if data drift is the cause. Retraining (C) is premature without root cause analysis. Increasing the threshold (B) ignores the underlying issue. Rolling back (D) may not help if the previous version also suffers from the same data drift.

Answer

Increase the prediction drift alert threshold to 0.4 to reduce the number of false alerts.

Answer

Retrain the model using the latest three months of data to incorporate recent trends.

Answer

Roll back to an earlier model version that had lower prediction drift.

Question 9

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?

Accepted Answer

Use Vertex AI Model Monitoring to compare the prediction distribution before and after the outage; significant drift indicates stale features.. Vertex AI Model Monitoring can detect prediction distribution drift, which directly indicates that the model is receiving different input features than expected. A significant drift after the outage, combined with the 30% drop in feature store read requests, confirms that stale or default features were substituted for missing lookups, causing the false positive rate to spike.

Answer

Monitor the prediction request latency to see if it remains low.

Answer

Verify the feature store's read throughput and latency metrics to ensure it is healthy.

Answer

Check the error rate for the endpoint; if no errors, then features were retrieved correctly.

Question 10

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?

Accepted Answer

Bayesian optimization (GP bandit). Bayesian optimization (GP bandit) is best for small trial budgets as it uses past results to guide search. Grid search would be too many combinations. Random search is better than grid but still less efficient than Bayesian. Vizier does not support simulated annealing.

Answer

Grid search

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Simulated annealing

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Random search

Question 11

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?

Accepted Answer

Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs.. ContainerComponent from kfp.v2.components allows you to define a custom container component by specifying the container image, command, inputs, and outputs directly. This is the appropriate approach when you have a custom training script that you want to containerize and use as a component in a Vertex AI Pipeline, as it gives you full control over the container configuration and artifact handling.

Answer

Use a pre-built Google Cloud Pipeline Component for Vertex AI Training with custom container configuration.

Answer

Define a Python function component with @dsl.component and include the container code inline.

Answer

Use the importer component to import the script and then run it as a task.

Question 12

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

Accepted Answer

Export the model to Vertex AI and create an endpoint. BigQuery ML models can be exported directly to Vertex AI for online prediction. Vertex AI provides a managed endpoint that supports real-time serving with low latency, which is required for online prediction. Exporting to Cloud Storage and then deploying to AI Platform is outdated because AI Platform is now part of Vertex AI, and the recommended path is to export the model directly to Vertex AI and create an endpoint.

Answer

Export the model to Cloud Storage and deploy to AI Platform

Answer

None of these; BigQuery ML models cannot be used for online prediction

Answer

Use BigQuery ML's ML.PREDICT for online predictions

Question 13

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?

Accepted Answer

Use Vertex AI Model Registry with automated deployment via Cloud Build, and restrict those permissions to the ML Engineering team using IAM conditions.. It combines Vertex AI Model Registry (which enforces that only validated models are promoted to production) with Cloud Build for automated deployment, and uses IAM conditions to restrict deployment permissions exclusively to the ML Engineering team. This ensures that models must pass the validation pipeline before deployment, and only authorized personnel can trigger the deployment process.

Answer

Use Vertex AI Workbench with user-managed notebooks.

Answer

Use custom roles with permissions to deploy models, and use Cloud Audit Logs to monitor deployments.

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Use Binary Authorization to ensure models are signed.

Answer

Use organization policies to restrict deployment to specific locations.

Question 14

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.)

Accepted Answer

Use traffic splitting to gradually shift traffic. Traffic splitting allows you to gradually shift a percentage of inference requests from the old model version to the new one. If the new version performs poorly, you can immediately revert the split to 0% for the new version, providing a fast and controlled rollback without redeploying or disrupting service.

Answer

Keep the old model version deployed alongside the new one

Answer

Configure Vertex AI Model Monitoring to compare predictions

Answer

Set up Cloud Monitoring alerts on model performance

Question 15

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.)

Accepted Answer

AutoMLTabularTrainingJobRunOp. AutoMLTabularTrainingJobRunOp is for AutoML training on tabular data, and BatchPredictOp is for batch predictions. Other options are for custom training or online prediction.

Answer

CustomJobRunOp

Answer

DataflowPythonOp

Answer

EndpointPredictOp