PMLE Practice Test 34 — 15 Questions

Question 1

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?

Accepted Answer

Use Vertex AI batch prediction with BigQuery source and sink.. Vertex AI batch prediction natively supports BigQuery as both source and sink, allowing you to run predictions on 10 TB of text data without any data movement or intermediate storage. This is the most cost-effective and simple approach because it eliminates the need for exporting data, managing infrastructure, or calling online endpoints, and it leverages Vertex AI's optimized batch inference infrastructure that scales automatically.

Answer

Use Cloud Run jobs to read from BigQuery and write results back.

Answer

Export BigQuery data to GCS, then run a Dataflow pipeline to call the model's online prediction endpoint for each row.

Answer

Use Cloud Dataproc to spin up a Spark cluster and run the model inference in parallel.

Question 2

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?

Accepted Answer

Compare the feature distribution in the training data with the recent serving data using statistical tests.. Comparing the feature distribution of the training data with recent serving data using statistical tests (e.g., Kolmogorov-Smirnov or Jensen-Shannon divergence) is the standard first step to quantify the drift and confirm it is statistically significant. This diagnostic action helps the team understand the nature and magnitude of the drift before deciding on remediation steps. Vertex AI Model Monitoring already performs such comparisons, but the team should independently verify the results in BigQuery to ensure accuracy.

Answer

Increase the frequency of model monitoring checks to every hour.

Answer

Increase the sampling rate for prediction logging to ensure full data capture.

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Reduce the alert threshold to minimize false positives.

Question 3

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?

Accepted Answer

Export batch predictions to BigQuery, and use Vertex AI Model Monitoring to compare prediction distributions against a baseline.. Vertex AI Model Monitoring can compare the distribution of batch prediction outputs (stored in BigQuery) against a baseline distribution to detect data drift or skew, which is the most direct way to proactively identify prediction inaccuracies. This approach monitors the statistical properties of predictions over time, catching shifts that could cause accuracy degradation even when the job runs successfully.

Answer

Use Cloud Monitoring to create a custom metric for prediction confidence and set an alert when confidence drops below 0.8.

Answer

Use Cloud Logging to export prediction requests and responses, then create a metric based on prediction count.

Answer

Enable Cloud Audit Logs to track when the batch prediction job runs and analyze the logs for anomalies.

Question 4

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?

Accepted Answer

Translation API. Translation API provides fast, real-time translation for text. Natural Language API is for analysis. Text-to-Speech is for audio. Vision API is for images.

Answer

Vision API

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Text-to-Speech API

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Natural Language API

Question 5

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?

Accepted Answer

Use Dataflow to preprocess the data and write the results to BigQuery or GCS, then launch a batch prediction job on the preprocessed data.. Dataflow (Apache Beam) is the recommended serverless service for distributed, scalable preprocessing of large datasets on Google Cloud. It can read raw data from GCS, apply custom Python preprocessing logic, and write the preprocessed results to GCS or BigQuery. The batch prediction job then reads the preprocessed data directly, avoiding the need to embed preprocessing in the prediction container or handle data on a single VM.

Answer

Use Cloud Functions to preprocess each record individually.

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Preprocess the data on a single VM and then upload to GCS.

Answer

Include the preprocessing logic in the custom container used for batch prediction.

Question 6

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?

Accepted Answer

Use a conditional component that checks data hash. The question asks for a pipeline component. Vertex AI Pipelines caching is a pipeline execution feature that can skip steps based on unchanged inputs, but it is not itself a component. A conditional component is a first-class component that can implement custom logic to check a data hash before deciding whether to run the training step, making it the best choice for this requirement. Other options: Cloud Functions are external, and Artifact Registry only stores models.

Answer

Use a caching mechanism in Vertex AI Pipelines

Answer

Use a Cloud Function to check BigQuery update time

Answer

Use Artifact Registry to store model versions

Question 7

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?

Accepted Answer

Use feature store caching with a larger cache size.. The best practice to reduce online serving latency for high-cardinality features in Vertex AI Feature Store is to enable caching with a larger cache size (Option D). Caching stores frequently accessed feature values in memory, reducing the need to query the BigQuery source for each prediction request. For high-cardinality features, a larger cache can hold more distinct values, minimizing cache misses. Option A (batch prediction) is not suitable for online serving. Option B (moving to Cloud Storage) does not integrate with Feature Store and may still have latency. Option C (increasing nodes) can improve throughput but does not directly address repeated access latency as effectively as caching.

Answer

Use batch prediction instead of online prediction.

Answer

Move the features to Cloud Storage and read them directly.

Answer

Increase the number of nodes in the feature store cluster.

Question 8

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?

Accepted Answer

Enable Vertex AI Model Monitoring for feature drift and set up alerts via Cloud Monitoring. Vertex AI Model Monitoring can monitor for feature distribution drift and skew, and can be configured to send alerts via Cloud Monitoring. Option A is part of model interpretation, not drift detection. Option C requires ground truth labels, which may not be available immediately. Option D is manual and not automated.

Answer

Schedule a batch prediction job daily and compare with ground truth

Answer

Use Vertex AI Explainable AI to understand predictions

Answer

Implement custom logging in the serving container and use BigQuery for analysis

Question 9

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?

Accepted Answer

min_replica_count=1, max_replica_count=20, target_cpu=60. Setting a high max_replica_count allows scaling to handle spikes, while a low min_replica_count saves cost during low traffic. CPU utilization target of 60% is reasonable.

Answer

min_replica_count=5, max_replica_count=5, target_cpu=60

Answer

min_replica_count=10, max_replica_count=10, target_cpu=60

Answer

min_replica_count=0, max_replica_count=100, target_cpu=80

Question 10

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?

Accepted Answer

Build a custom container with PyTorch JIT and deploy it on Vertex AI Prediction.. A custom container with PyTorch JIT allows full control over model execution, including dynamic control flow and custom kernels, and can be deployed on Vertex AI Prediction, which supports custom containers for low-latency inference. Option B is wrong because converting to TensorFlow SavedModel would lose PyTorch-specific features like custom JIT kernels. Option C is wrong because Cloud Functions have cold start latency and are not suited for low-latency production inference at scale. Option D is wrong because the prebuilt PyTorch container may not support custom JIT kernels or dynamic control flow optimally.

Answer

Convert the model to TensorFlow SavedModel and serve it on Vertex AI Prediction with TensorFlow Serving.

Answer

Use Cloud Functions with a PyTorch wrapper to handle inference requests.

Answer

Deploy the model on Vertex AI Prediction using the prebuilt PyTorch container.

Question 11

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?

Accepted Answer

Analyze the input data to understand if there is a skew or drift.. When a monitoring alert triggers, the first step is to investigate the root cause: check if input data has changed, retraining is needed, or there is a data pipeline issue. Simply rolling back or retraining without analysis might be premature.

Answer

Roll back the model to the previous version immediately.

Answer

Increase the alerting threshold to reduce false positives.

Answer

Retrain the model using the latest data and redeploy.

Question 12

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

Accepted Answer

To optimize models for deployment by quantizing and compiling them. Vertex AI Model Optimization automatically quantizes and compiles models to reduce latency and memory footprint for serving.

Answer

To monitor model performance in production

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To search for optimal hyperparameters

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To train models faster using distributed training

Question 13

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?

Accepted Answer

Use Vertex AI Forecasting to train a time-series model with holiday and weather features. Vertex AI Forecasting is purpose-built for time-series forecasting with support for exogenous features like holidays and weather, making it the ideal choice for weekly sales predictions across 500 stores. It handles multiple time series automatically and integrates with the required weekly retraining cycle, unlike generic regression models that lack temporal awareness.

Answer

Use BigQuery ML to create a linear regression model on historical data

Answer

Export data to AutoML Tables and train a regression model

Answer

Build a custom LSTM model using TensorFlow on Vertex AI Workbench

Question 14

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?

Accepted Answer

Model A's machine type has insufficient CPU and GPU for the load.. Model A has only one GPU and fewer CPU cores compared to Model B. During high traffic, Model A's resources become a bottleneck. The traffic split is equal, so both get similar load, but Model A's hardware is weaker.

Answer

Model A is not autoscaling properly due to minReplicaCount=1.

Answer

Dedicated endpoint is disabled, causing resource sharing between models.

Answer

The traffic split is unevenly balanced, causing Model A to receive more requests.

Question 15

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?

Accepted Answer

Use Vertex AI Model Monitoring with skew detection enabled.. Vertex AI Model Monitoring with skew detection enabled is the correct approach because it is specifically designed to detect data drift between training and serving datasets in a continuous training pipeline. It automatically computes distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) for each feature and compares the training data distribution against the serving data distribution, triggering alerts when drift exceeds a configured threshold. This integrates natively with Vertex AI Pipelines, enabling automated retraining workflows.

Answer

Export metrics to Cloud Monitoring and set up alerting on mean values.

Answer

Use Cloud DLP to inspect the datasets and generate summary statistics.

Answer

Compute histograms of features in BigQuery ML and compare them manually.