PMLE Practice Test 12 — 15 Questions

Question 1

Your company deploys batch prediction jobs using Vertex AI Batch Prediction. You need to monitor the jobs for failures and performance. What is the recommended approach?

Accepted Answer

Use Cloud Monitoring to create custom dashboards and alerts based on Vertex AI batch prediction metrics.. Cloud Monitoring (formerly Stackdriver) is the native Google Cloud service for collecting, visualizing, and alerting on metrics from Vertex AI, including batch prediction job success rates, latency, and resource utilization. It provides pre-built dashboards and the ability to create custom alerts, making it the recommended approach for monitoring failures and performance in a centralized, scalable way.

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Use Cloud Logging to export batch prediction logs and create log-based metrics.

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Set up email alerts in the Vertex AI console for failed jobs.

Answer

Enable the Recommender to get optimization suggestions for batch jobs.

Question 2

In a Vertex AI Pipeline, a component produces a Metrics artifact that includes an evaluation metric. The engineer wants to use this metric value as a condition to decide whether to deploy the model. However, the metric value is stored in the artifact's metadata and not directly as a pipeline parameter. How can the engineer pass the metric value to a downstream conditional task?

Accepted Answer

Add a component that reads the artifact's metadata and outputs the metric as a parameter, then use that parameter in the condition.. Vertex AI Pipeline conditions require pipeline parameters (typed values) to evaluate expressions like `dsl.If`. A Metrics artifact's metadata is stored as an artifact property, not a pipeline parameter, so a custom component must read that metadata and output the metric as a parameter. This parameter can then be used in the `dsl.If` condition to control downstream deployment.

Answer

Configure the component that produces the Metrics artifact to also output the metric as a pipeline parameter.

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Use the importer component to convert the artifact into a parameter.

Answer

Use the artifact directly in the dsl.If condition, as artifacts are comparable.

Question 3

An engineer is configuring Vertex AI Model Monitoring for a model deployed on an endpoint. They want to monitor feature skew using the training dataset as a baseline. The training dataset is large (10 TB). What is the most efficient way to provide the baseline distribution?

Accepted Answer

Use the BigQuery table containing the training data as the baseline source in the monitoring configuration. Vertex AI Model Monitoring can automatically compute the baseline distribution from the training dataset stored in BigQuery. This avoids manual computation or exporting large datasets.

Answer

Compute the baseline distribution offline and upload a JSON file with feature statistics

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Upload the entire training dataset as a CSV file to Cloud Storage and reference it in the monitoring config

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Sample 1% of the training data and use that as baseline

Question 4

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories (CSR) is the correct choice because it provides Git-based version control for notebooks, enabling teams to track changes, collaborate, and maintain a full version history. Vertex AI Workbench integrates natively with CSR, allowing users to clone, commit, and push notebook files directly from the JupyterLab interface, which is essential for collaborative development with revision tracking.

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Artifact Registry

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Cloud Storage

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Data Catalog

Question 5

A financial services firm deploys a binary classification model for fraud detection. The model's precision is 0.95 and recall is 0.60 on the test set. After deployment, the fraud rate in production is 0.5% compared to 5% in the test set. The model shows good calibration on the test set (Brier score 0.02) but poor calibration in production (Brier score 0.15). What is the most likely explanation for the calibration degradation?

Accepted Answer

The relationship between features and the target has changed (concept drift), causing the model's probability estimates to be misaligned with the true probabilities.. The model's calibration degrades in production despite being well-calibrated on the test set, which had a 5% fraud rate, while production has a 0.5% fraud rate. This shift in class imbalance (prior probability shift) directly affects the model's probability estimates because the model's predicted probabilities are conditional on the training distribution. Option D is correct because concept drift—specifically a change in the base rate of fraud—causes the model's probability estimates to no longer reflect the true posterior probabilities in production, leading to a higher Brier score.

Answer

The distribution of input features has shifted significantly, causing the model to produce incorrect probabilities.

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The model overfits to noise in the training data, leading to poor generalization.

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The production data has a different class imbalance than the training data, causing the model to be biased toward the majority class.

Question 6

A machine learning engineer wants to monitor model performance on Vertex AI for a regression model. Which metric is most appropriate to track the average prediction error?

Accepted Answer

RMSE. RMSE (Root Mean Squared Error) is the most appropriate metric for tracking average prediction error in a regression model because it measures the standard deviation of residuals (prediction errors) in the same units as the target variable. On Vertex AI, RMSE is a built-in evaluation metric for regression models, directly quantifying how far predictions deviate from actual values on average.

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F1 score

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Precision

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Accuracy

Question 7

A team is training a custom TensorFlow model on Vertex AI using a pre-built container. They need to use a TPU pod slice (v3-32). What THREE actions are required to set up the training job correctly?

Accepted Answer

Configure TF_CONFIG for distributed training. Options A and C are correct because TPU pod slices on Vertex AI require setting TF_CONFIG for distributed training (A) and specifying the accelerator type as TPU_V3 with the appropriate topology (C). Option D is not required: Vertex AI provides pre-built containers for TensorFlow that already include TPU support, so a custom container is unnecessary. Using a custom container would be an extra step not needed for this scenario.

Answer

Set the training worker pool to use only one worker

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Use a custom container with TensorFlow 2.12 and TPU support

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Set the machine type to a high-memory VM with NVIDIA A100 GPUs

Question 8

An organization uses Cloud Composer to orchestrate ML workflows. A DAG that triggers Vertex AI training jobs fails because the training job exceeds the 7-day maximum runtime. What is the best way to handle long-running training jobs in Cloud Composer?

Accepted Answer

Use Vertex AI Pipeline to manage the training job asynchronously. Vertex AI Pipelines natively supports asynchronous execution, allowing Cloud Composer to trigger a pipeline and monitor its status without blocking the Airflow worker for the entire duration of the training job. This decouples the DAG execution timeout from the training runtime, enabling workflows that exceed the 7-day Airflow task timeout limit.

Answer

Increase the DAG execution timeout to 14 days in the Airflow configuration

Answer

Refactor the training job to run on Dataflow, which supports longer runtimes

Answer

Set max_active_runs=1 in the DAG to prevent overlapping runs

Question 9

Which of the following is a best practice when designing idempotent pipeline components in Vertex AI?

Accepted Answer

Pass data through Cloud Storage URIs rather than in-memory.. Passing data through Cloud Storage URIs ensures that component outputs are stored persistently and can be retrieved by downstream components, even if the original component instance is terminated or scaled down. This aligns with the principle of idempotency because the same input will always produce the same output stored at the same URI, and re-running the component will not cause side effects or data loss. In contrast, in-memory data is ephemeral and tied to a specific runtime instance, breaking idempotency across retries or parallel executions.

Answer

Use global variables to share state between components.

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Write component outputs to a database with timestamps.

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Use the same output name for all runs to avoid duplication.

Question 10

A company is using Vertex AI Pipelines to automate model retraining. They have a component that creates a BigQuery table with training data. To ensure idempotency, the component should check if the table already exists and recreate it if necessary. What is the best practice for passing data between pipeline components?

Accepted Answer

Store data as artifacts in Cloud Storage and pass the GCS URI between components.. Vertex AI Pipelines is designed to pass data between components via Cloud Storage artifacts. By storing the BigQuery table metadata or training data as a file in Cloud Storage and passing the GCS URI as an artifact, the pipeline ensures idempotency and decouples components. This approach aligns with Kubeflow Pipelines' artifact-based I/O model, where each component's outputs are materialized as URIs rather than in-memory objects.

Answer

Pass data in-memory as Python objects between components.

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Use BigQuery table names as component outputs and inputs.

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Use Cloud SQL to store intermediate results and pass connection strings.

Question 11

A data science team has trained a large deep learning model using Vertex AI Workbench. They want to deploy it to Vertex AI Prediction for online serving. The model is stored in a custom container with a Python-based web server. Which TWO actions should the team take to ensure optimal performance and cost?

Accepted Answer

Request GPU machine types for the prediction nodes.. B is correct because deep learning models, especially large ones, benefit significantly from GPU acceleration for online inference due to their parallel processing capabilities. Vertex AI Prediction supports GPU machine types, and using them reduces latency and improves throughput for compute-intensive model serving, which is critical for optimal performance.

Answer

Configure the model to use a larger batch size for inference.

Answer

Set the container's health check path to '/predict'.

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Use a global load balancer to distribute traffic across regions.

Question 12

You are using Cloud Datalab for collaborative data exploration with your team. However, some team members cannot access the Datalab instances. What is the most likely issue?

Accepted Answer

The team members have not been granted the necessary IAM roles (e.g., roles/datalab.user) on the project.. Cloud Datalab uses IAM permissions to control access to instances. The most common reason team members cannot access Datalab instances is that they lack the necessary IAM role, such as `roles/datalab.user`, which grants permission to view and connect to Datalab instances. Without this role, even if the instances exist and are running, users will receive permission-denied errors when trying to access them via the Datalab UI or API.

Answer

The Datalab instances have been deleted by another team member.

Answer

The team members need to install the Cloud Datalab SDK locally.

Answer

The Datalab instances were created using an incompatible notebook type.

Question 13

A data scientist has trained a model using Vertex AI Training and wants to deploy it to a Vertex AI Endpoint for online predictions. Which orchestration service should be used to automate the deployment step after training completes?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the correct orchestration service because it is purpose-built for automating and managing end-to-end ML workflows on Google Cloud. It allows you to define a pipeline that includes both the training step (using Vertex AI Training) and the subsequent deployment step (creating or updating a Vertex AI Endpoint) as a single, repeatable, and monitored workflow. This ensures that after training completes, the model is automatically deployed without manual intervention, leveraging the pipeline's ability to pass artifacts and trigger conditional logic.

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App Engine

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Cloud Functions

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Cloud Build

Question 14

Which THREE considerations are important when setting up a shared feature store in Vertex AI Feature Store for multiple teams?

Accepted Answer

Enable feature monitoring for data quality and freshness. Vertex AI Feature Store provides built-in feature monitoring that tracks data quality metrics (e.g., fraction of null values, distribution drift) and freshness (e.g., staleness of feature values). Enabling this monitoring is critical when multiple teams share a feature store to ensure that features remain reliable and up-to-date for downstream models, preventing silent degradation.

Answer

Use separate BigQuery tables for each team's features

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Allow each team to build independent ingestion pipelines

Question 15

A company is implementing MLOps with Vertex AI. They need to ensure that only approved models can be deployed to production. Which TWO practices should they adopt?

Accepted Answer

Use Vertex AI Model Registry aliases (e.g., 'champion') to mark production-ready models. Using aliases to designate champion and enabling manual approval in Continuous Evaluation ensures governance.

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Use a single endpoint for all models without versioning

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Allow any user with Vertex AI User role to deploy models

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Store all models in Cloud Storage without registry