PMLE Timed Practice Test 5 — 10 Questions

Question 1

Match each ML pipeline component to its description.

Question 2

Match each Google Cloud storage option to its best use case.

Question 3

A team of data scientists and ML engineers is collaborating on a project using Vertex AI Workbench. They need to share notebooks and code, but want to avoid conflicts and maintain a history of changes. Which approach should they use?

Accepted Answer

Use a git repository (e.g., Cloud Source Repositories) to manage code and notebooks.. Option D is correct because using a git repository (e.g., Cloud Source Repositories) provides version control, branching, and a full history of changes, which is essential for collaborative development. This approach avoids conflicts by allowing team members to work on separate branches and merge changes systematically, unlike shared storage or manual methods that lack conflict resolution and audit trails.

Answer

Email notebook files to each other and manually merge changes.

Answer

Store notebooks in a shared Cloud Storage bucket and access them simultaneously.

Answer

Use Vertex AI Experiments to share notebook outputs.

Question 4

A data science team uses a shared Cloud Storage bucket to store training datasets. They notice that some team members accidentally overwrite existing datasets, causing issues with reproducibility. Which approach best prevents accidental overwrites while maintaining collaboration?

Accepted Answer

Enable object versioning on the bucket and use lifecycle rules to manage versions.. Option D is correct because enabling object versioning on a Cloud Storage bucket preserves all versions of an object, so even if a team member overwrites a dataset, the previous version remains accessible. This maintains collaboration (anyone can upload) while preventing permanent data loss. Lifecycle rules can then be used to manage storage costs by automatically deleting old versions after a specified period.

Answer

Use a single shared service account with strict IAM roles that allow only append operations.

Answer

Require team members to manually rename files before uploading.

Answer

Set bucket permissions to read-only for all team members except the data owner.

Question 5

A machine learning engineer needs to share a trained model with the product team for integration. The model is stored in Cloud Storage, and the product team’s service account needs read access. The engineer wants to follow the principle of least privilege. Which IAM configuration should be used?

Accepted Answer

Grant the product team's service account the roles/storage.objectViewer role at the bucket level.. Option B is correct because granting the product team's service account the roles/storage.objectViewer role at the bucket level provides read-only access to objects in that specific bucket, adhering to the principle of least privilege. This role allows the service account to list and read objects without granting broader permissions, such as modifying or deleting them, and scoping it to the bucket prevents unnecessary access to other buckets in the project.

Answer

Generate a signed URL with read access and share it with the product team.

Answer

Grant the product team's service account the roles/storage.objectAdmin role at the bucket level.

Answer

Grant the product team's service account the roles/storage.objectViewer role at the project level.

Question 6

A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the correct feature because it captures parameters, metrics, and artifacts for each pipeline run, enabling reproducibility and lineage tracking. This directly supports the team's need to ensure runs are reproducible and artifacts are tracked, as Experiments automatically logs metadata for every execution.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Endpoints

Question 7

A machine learning team has a prototype using a custom TensorFlow model trained on a small dataset stored in Cloud Storage. They want to scale the prototype to production with minimal code changes while ensuring the model can handle increased traffic and new data. The model currently loads data using tf.data.Dataset from CSV files. Which approach best meets these requirements?

Accepted Answer

Use Vertex AI Training with hyperparameter tuning and distributed training, then deploy the model to Vertex AI Prediction with autoscaling.. Vertex AI Prediction with autoscaling directly addresses the need to handle increased traffic without code changes, while Vertex AI Training with hyperparameter tuning and distributed training enables scaling to larger datasets with minimal modifications to the existing tf.data pipeline. This approach keeps the custom TensorFlow model intact and leverages managed infrastructure for both training and serving.

Answer

Deploy the model to AI Platform (Unified) Prediction with a custom container, and use AI Platform Training to retrain on larger datasets.

Answer

Migrate the model to BigQuery ML and use SQL for training and prediction to leverage BigQuery's scalability.

Answer

Package the model as a Cloud Run Function and use Cloud Scheduler to trigger retraining periodically.

Question 8

A developer creates a Cloud Build trigger that runs a training pipeline whenever code is pushed to the main branch of the repository. The trigger is configured to use a source archive stored in Cloud Storage. After pushing code to main, the build fails with the error shown. What is the most likely cause of this failure?

Accepted Answer

The source archive is not being updated when code is pushed, so the trigger tries to fetch an old or nonexistent object.. Option C is correct because the trigger is configured to use a source archive stored in Cloud Storage. When code is pushed to the main branch, the trigger attempts to fetch the archive from the specified Cloud Storage location. If the archive is not updated (e.g., via a separate upload or a Cloud Function that rebuilds the archive on push), the trigger will either fetch an old version or fail if the object does not exist. The error indicates that the build cannot proceed because the source archive is stale or missing, not because of a missing config file or permission issue.

Answer

The build configuration file is missing from the source archive.

Answer

The included files filter 'train/**' excludes all files outside the train directory, causing the build to have no source.

Answer

The service account does not have storage.objectViewer permission on the bucket.

Question 9

A healthcare company uses AutoML Tables to predict patient readmission risk. The dataset contains 500,000 rows and 200 features, including patient demographics, lab results, and medical history. The model accuracy is lower than expected. The engineer wants to improve performance using low-code techniques. Which THREE actions are most effective? (Choose THREE.)

Accepted Answer

Remove highly correlated features using AutoML Tables' built-in feature importance analysis.. Option B is correct because AutoML Tables provides built-in feature importance analysis that can identify and remove highly correlated features, which reduces noise and multicollinearity, often improving model performance without manual intervention. This is a low-code technique that leverages the platform's automated capabilities to streamline feature selection.

Answer

Increase the training time budget to the maximum allowed.

Answer

Use a custom model architecture via AutoML Tables advanced options.

Question 10

A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?

Accepted Answer

Jensen-Shannon divergence (JSD). Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.

Answer

Pearson correlation coefficient

Answer

Chi-squared statistic

Answer

Population Stability Index (PSI)