PMLE Practice Test 32 — 15 Questions

Question 1

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?

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

Accepted Answer

Share the model ID and grant colleagues the 'vertex.ai.models.get' permission.. Vertex AI Model resources are managed within a single Google Cloud project, and the recommended way to share a model without exposing the underlying project is to grant the IAM role 'roles/aiplatform.user' or the specific permission 'vertex.ai.models.get' to the colleagues' Google accounts. This allows them to access the model via the model ID (a fully qualified resource name like 'projects/{project}/locations/{region}/models/{model}') without needing to copy or expose the project's infrastructure or credentials.

Answer

Create a new project and copy the model.

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Upload the model to a public Cloud Storage bucket.

Answer

Export the model artifact and email it.

Question 2

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?

Accepted Answer

The learning rate is too high for the batch size.. When scaling from a single GPU to 8 TPU cores, the global batch size increases from 32 to 256. The learning rate of 1e-4, which was appropriate for batch size 32, becomes too high for the larger batch size. This violates the linear scaling rule (learning rate should be scaled proportionally to batch size), causing gradient updates to overshoot minima and leading to NaN loss and divergence.

Answer

The batch size is too small for TPU.

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The learning rate schedule should be cosine instead of linear.

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The warm-up steps are insufficient.

Question 3

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)

Accepted Answer

Deploy the challenger model (v2) to the same endpoint as the champion (v1).. Deploying both v1 and v2 to the same Vertex AI endpoint allows you to use the built-in traffic splitting feature. This enables you to route a percentage of requests to each model version without managing separate endpoints or DNS changes, which is the standard approach for A/B testing on Vertex AI.

Answer

Create a new endpoint for v2 and gradually shift DNS traffic.

Answer

Use Vertex AI Experiments to compare model performance.

Question 4

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?

Accepted Answer

Configure the 'train-exec' executor to use a machine type with higher memory.. In Vertex AI pipelines, memory allocation is defined at the executor level, not directly on the task. The 'train' task references the 'train-exec' executor, which specifies the machine type and thus the available memory. To resolve the resource exhaustion error without increasing overall quota cost, the engineer should change the machine type in the executor specification to a memory-optimized type (e.g., n1-highmem-*). Options A and C are incorrect because Vertex AI pipelines do not support setting a 'memory' field directly on the task. Option D is incorrect because changing the accelerator type does not address memory exhaustion.

Answer

Add a 'memory' field to the train task specification.

Answer

Increase the memory of the train task to 32 GiB.

Answer

Set 'acceleratorType' to 'NVIDIA_TESLA_T4' on the train task.

Question 5

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)

Accepted Answer

Configure the endpoint to use custom metrics from Cloud Monitoring.. Cloud Monitoring custom metrics allow you to define autoscaling based on signals that are directly relevant to your inference latency, such as request queue depth or model throughput. This enables the autoscaler to react to real-time demand more precisely than CPU or memory utilization alone, which is critical for meeting strict P99 latency targets.

Answer

Use a regional endpoint to reduce network latency.

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Enable GPU acceleration for faster inference.

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Set minReplicas to 0 to save cost.

Question 6

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?

Accepted Answer

The incoming data distribution has changed, e.g., due to a new marketing campaign attracting different customers.. A sudden increase in prediction requests alongside a decrease in predicted churn probabilities, while validation accuracy remains stable, indicates a shift in the incoming data distribution (covariate shift). This is typical when a new marketing campaign attracts a different customer segment that inherently has lower churn risk. The model itself hasn't degraded; it's simply seeing a different population than it was trained on, which changes the base rate of churn in the live traffic.

Answer

A training-serving skew exists between the training pipeline and the serving endpoint.

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Concept drift has occurred, changing the relationship between features and churn.

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Data leakage during training caused the model to overfit to historical patterns.

Question 7

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?

Accepted Answer

Cloud Monitoring + Cloud Functions + Vertex AI Pipeline. Cloud Monitoring alert policies can be configured to send webhook notifications directly to a Cloud Function's HTTP endpoint, triggering the function without requiring Pub/Sub. The Cloud Function then initiates a Vertex AI Pipeline to retrain the model. This is the minimal set of services needed; adding Pub/Sub (as in option D) would be unnecessary.

Answer

Cloud Monitoring + Cloud Functions + Vertex AI Endpoint

Answer

Cloud Logging + Cloud Scheduler + Vertex AI Training

Answer

Cloud Monitoring + Cloud Pub/Sub + Cloud Functions + Vertex AI Pipeline

Question 8

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?

Accepted Answer

The pipeline is issuing too many concurrent queries; use a BigQuery reservation to guarantee slot capacity. The 403 'Exceeded rate limits' error in BigQuery indicates that the project is hitting the concurrent query rate limit or the rate of bytes read per second. Using a BigQuery reservation guarantees dedicated slot capacity, which prevents rate-limit errors by ensuring the pipeline has consistent compute resources regardless of other workloads in the project. This is the most direct solution because rate limits are enforced at the project level based on available slots, and a reservation provides a fixed number of slots that bypass those limits.

Answer

The training dataset is too large; partition the table and query only the latest partition

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The pipeline step timeout is too short; increase the timeout to 30 minutes

Answer

The SQL query is inefficient; rewrite it using materialized views

Question 9

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

Accepted Answer

Use the AutoML recommended data split (train/validation/test) to avoid overfitting.. AutoML's recommended data split (train/validation/test) is designed to prevent overfitting by ensuring the model is evaluated on unseen data. AutoML automatically handles the split ratio (e.g., 80/10/10) and stratification, which is a best practice for low-code ML solutions where manual split logic is error-prone.

Answer

Impute missing values manually before uploading the dataset.

Answer

Normalize numerical features to zero mean and unit variance.

Answer

Export the data and train a custom model with a different architecture.

Question 10

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?

Accepted Answer

The serving input function in the container is not applying the same normalization as during training.. A constant factor error typically indicates a preprocessing mismatch, such as different normalization. Option A is wrong because training vs evaluation mode affects dropout and batch normalization, which can cause different outputs but not a constant factor scaling. Option C is wrong because different PyTorch versions may cause other inconsistencies but not a constant factor. Option D is possible but less specific than preprocessing.

Answer

The model is loaded in evaluation mode, but the training mode was used in testing.

Answer

The container is using a different PyTorch version than the training environment.

Answer

There is a bug in the custom container's prediction route.

Question 11

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?

Accepted Answer

Use Vertex AI batch prediction with BigQuery as the source and sink.. Vertex AI batch prediction natively supports BigQuery as both input and output, eliminating the need for data export or custom pipelines. For 10 million rows at 100ms each, batch prediction processes them in parallel across multiple machines, minimizing execution time while avoiding the per-node costs of online prediction or the overhead of managing Dataflow or GKE clusters.

Answer

Export the BigQuery data to CSV in GCS, then run a custom Dataflow pipeline to make predictions.

Answer

Use Vertex AI online prediction and send all rows as separate requests.

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Use a custom container running on Google Kubernetes Engine to perform inference.

Question 12

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?

Accepted Answer

Use Vertex AI Model Monitoring to detect drift and trigger a pipeline. Vertex AI Model Monitoring can be configured to detect data drift on the model's input features, and when drift exceeds a threshold, it can trigger a Cloud Function or a Vertex AI pipeline to retrain the model. This approach avoids unnecessary retraining when the data distribution has not changed significantly, which is more efficient than always retraining. The integration with Cloud Functions or Pub/Sub allows for a serverless, event-driven retraining pipeline that only runs when needed.

Answer

Use a scheduled pipeline that always retrains

Answer

Use Cloud Monitoring alerts on data drift to trigger retraining

Answer

Use Cloud Functions on schedule to compare distributions

Question 13

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.)

Accepted Answer

Cloud Functions. Cloud Functions is correct because it can be triggered directly by Cloud Storage events (e.g., object finalize/create) to start a Vertex AI Pipeline run when new data arrives, enabling event-driven continuous training without manual intervention. Cloud Storage is correct because it serves as the source of new data and its event notifications (via Pub/Sub) are the trigger mechanism that Cloud Functions subscribes to, forming the core event-driven architecture.

Answer

Cloud Scheduler

Answer

Vertex AI Experiments

Answer

Cloud Composer

Question 14

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?

Accepted Answer

Implement a two-tower model (user and item towers) with embeddings and nearest neighbor search. The two-tower model (user and item towers) with embeddings and nearest neighbor search is the most suitable because it handles cold-start users and new articles by learning separate embeddings for users and items, enabling efficient retrieval via approximate nearest neighbor (ANN) search. This architecture supports real-time updates and scales to 10M+ users by decoupling user and item representations, allowing incremental training on new interactions without full retraining.

Answer

Deploy a Deep Learning Recommendation Model (DLRM) for prediction

Answer

Use a contextual bandit algorithm for exploration only

Answer

Use matrix factorization with collaborative filtering

Question 15

A machine learning engineer needs to schedule a Vertex AI pipeline to run daily at midnight. Which approach should they use?

Accepted Answer

Create a Cloud Function that calls the Vertex AI API, triggered by a Pub/Sub message from Cloud Scheduler.. Vertex AI Pipelines does not natively support cron scheduling. The recommended pattern is to use Cloud Scheduler to publish a message to a Pub/Sub topic at the desired time, which then triggers a Cloud Function that calls the Vertex AI API to create and run the pipeline. This decoupled architecture ensures reliable scheduling and allows for custom logic before invocation.

Answer

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

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Create a Cloud Build trigger that runs the pipeline on a schedule.

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Use Cloud Tasks to create a recurring task that invokes the pipeline.