PMLE Collaborating to manage data and models Practice Test 3 — 15 Questions

Question 1

A financial services company stores its training data in Cloud Storage and uses Vertex AI Pipelines for model training. The security team requires that every training run be traceable to the exact dataset version used, and that data scientists cannot alter artifacts after a pipeline run completes. The team wants to enforce this with minimal custom code. What should they do?

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

Accepted Answer

Configure the pipeline to write metadata to Vertex ML Metadata and set the Cloud Storage bucket to have Object Versioning enabled with a retention policy.. The correct answer combines Vertex ML Metadata for automatic lineage tracking with Cloud Storage Object Versioning and a retention policy for immutability. Vertex ML Metadata captures dataset versions as pipeline artifacts, while Object Versioning ensures old data versions are retained and cannot be altered. This meets both traceability and immutability requirements with minimal custom code.

Answer

Store all training data in BigQuery and use table snapshots for each pipeline run.

Answer

Enable Cloud Audit Logs for data access and configure a log sink to BigQuery for analysis.

Answer

Use a Cloud Storage bucket with Uniform Bucket-Level Access and grant data scientists the Storage Object Viewer role.

Question 2

Your team uses Vertex AI Model Registry to manage model versions. A colleague accidentally deployed a model version that was not approved for production. You need to prevent unauthorized deployments in the future while allowing approved versions to be deployed. What should you do?

Accepted Answer

Use IAM conditions to grant deploy permissions only for models with a specific label indicating approval.. To enforce that only approved models are deployed, you can use IAM conditions to restrict the deploy permission based on a model label. This approach leverages Google Cloud's fine-grained access control, ensuring that only models with the approval label can be deployed, while still allowing approved deployments.

Answer

Set the model version's state to 'DEPLOYED' only after approval, and restrict deployment to that state.

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Create a separate project for production models and grant deploy permissions only there.

Answer

Enable Vertex AI Model Registry's built-in approval workflow to require manual approval before deployment.

Question 3

A data science team uses Vertex AI Feature Store to manage features for multiple models. They need to ensure that when a feature's definition changes, all dependent models are retrained with the updated feature. What should they do?

Accepted Answer

Use Vertex ML Metadata to track feature versions and set up a Cloud Scheduler job to retrain models when a new feature version is registered.. Tracking feature versions with Vertex ML Metadata and using Cloud Scheduler to trigger retraining pipelines provides an automated, traceable solution. When a new feature version is registered, the scheduler can invoke a pipeline that retrains all dependent models, ensuring consistency and reducing manual effort.

Answer

Store feature definitions in a shared Cloud Storage bucket and have each model owner monitor the bucket for changes.

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Manually notify all model owners via email whenever a feature is updated.

Answer

Enable automatic retraining for all models when a feature change is detected.

Question 4

Which TWO statements about Vertex AI Feature Store are correct? (Choose 2)

Accepted Answer

Feature Store provides a centralized repository for feature data.. Option D is correct because Vertex AI Feature Store acts as a centralized repository where feature data is registered, versioned, and managed as feature groups and features, so multiple teams and models can share a single consistent source of features. Option E is correct because Feature Store supports both online serving, which returns low-latency feature values for real-time predictions, and offline serving, which reads historical feature values in bulk for training and batch scoring. Options A, B, and C are incorrect: Feature Store does not automatically perform feature engineering transformations (you define and ingest the transformed values yourself), it is not limited to numerical features (it supports types such as strings, booleans, and arrays as well), and it is not restricted to Vertex AI models since features can be served to any application or model via the online/offline APIs.

Answer

Feature Store automatically applies feature engineering transformations.

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Feature Store can only store numerical features.

Answer

Feature Store can only be used with Vertex AI models.

Question 5

Which THREE actions are best practices for managing ML models in production on Google Cloud? (Choose 3)

Accepted Answer

Monitor model performance and data drift continuously.. Option B is correct because continuous monitoring of model performance and data drift is essential in production to detect degradation and trigger retraining before business impact occurs. Option C is correct because a central model registry (such as Vertex AI Model Registry) provides governance, lineage, and controlled promotion of models across environments. Option D is correct because versioning model artifacts and training datasets ensures reproducibility, traceability, and rollback capability for every deployed model. Option A is not a best practice because manual hyperparameter tuning does not scale and should be automated with tools like Vertex AI Vizier. Option E is not a best practice because retaining all raw training data indefinitely increases cost and compliance risk; retention should follow defined policies and lifecycle rules.

Answer

Manually tune hyperparameters for each retraining run.

Answer

Store all raw training data indefinitely for auditability.

Question 6

A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive data stored in BigQuery that includes protected health information (PHI). The data science team uses Vertex AI Workbench notebooks to explore the data and develop models. The organization's security policy requires that all PHI data must be encrypted at rest and in transit, and that access to the data is logged and audited. They also need to ensure that the data used for model training is de-identified to remove direct identifiers such as patient names and SSNs. The team wants to automate the de-identification process as part of the data pipeline. Which approach meets these requirements?

Accepted Answer

Create a Dataflow pipeline that reads from the original BigQuery table, applies Cloud DLP de-identification transforms, and writes to a new BigQuery table. Grant the data science team access to the de-identified table.. It uses Cloud DLP within a Dataflow pipeline to automatically de-identify PHI data as it is read from the original BigQuery table and written to a new, de-identified table. This satisfies the requirement for automated de-identification, while the original table remains encrypted at rest (BigQuery default) and in transit (TLS), and access to the original data can be logged via Cloud Audit Logs. The data science team only gets access to the de-identified table, ensuring PHI is not exposed during model development.

Answer

Enable Shielded VM on Vertex AI Workbench notebooks and use VPC-SC to restrict data access.

Answer

Use Cloud Key Management Service to encrypt the PHI columns in BigQuery, and share the encryption key with the data science team.

Answer

Use BigQuery row-level security to mask PHI columns for the data science team, and train the model directly on the original table.

Question 7

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

Accepted Answer

Export the model, then upload to GCS, then register as a model, then deploy to endpoint, then test. Export the model, upload to GCS, register as a model, deploy to endpoint, then test.

Answer

Upload to GCS, then export the model, then register as a model, then deploy to endpoint, then test

Answer

Export the model, then register as a model, then upload to GCS, then deploy to endpoint, then test

Answer

Export the model, then upload to GCS, then deploy to endpoint, then register as a model, then test

Question 8

A team of ML engineers is collaborating on a project using Vertex AI. They want to ensure that only approved models are deployed to production. Which approach should they use?

Accepted Answer

Use Vertex AI Model Registry with version aliases to manage model versions and promote them after approval.. Vertex AI Model Registry provides a centralized repository for managing model versions, with support for version aliases (e.g., 'champion', 'challenger') that allow teams to promote models to production only after approval. This ensures governance and traceability, meeting the requirement that only approved models are deployed.

Answer

Store all models in a Cloud Storage bucket and manually control access via IAM permissions.

Answer

Deploy models directly from training jobs to an endpoint without version tracking.

Answer

Use Cloud Dataflow to transform raw predictions and then store them in BigQuery for analysis.

Question 9

A company uses a Cloud Composer DAG to run a daily ML pipeline that includes Dataflow jobs and model training on Vertex AI. The pipeline frequently fails due to insufficient permissions when the Dataflow worker accesses data in Cloud Storage. What is the most efficient way to resolve this issue?

Accepted Answer

Create a custom service account with required permissions and assign it to the Dataflow job.. The most efficient way to resolve insufficient permissions for Dataflow workers accessing Cloud Storage is to create a custom service account with the required roles (e.g., roles/storage.objectViewer) and assign it to the Dataflow job via the --serviceAccount option. This follows the principle of least privilege and ensures that only the Dataflow workers have the necessary permissions, without affecting other pipeline components or exposing the bucket publicly.

Answer

Grant the 'roles/storage.objectViewer' role to 'allUsers' on the Cloud Storage bucket.

Answer

Use the Composer environment's service account for all pipeline components.

Answer

Move the Dataflow job to run after the pipeline so that data is already processed.

Question 10

A data scientist wants to share a trained model with the team for review before deployment. The model is stored in Vertex AI Model Registry. What is the recommended way to grant the team read access to the model?

Accepted Answer

Grant the IAM role 'roles/aiplatform.viewer' to the team members on the project.. The 'roles/aiplatform.viewer' IAM role grants read-only access to Vertex AI resources, including models in the Model Registry. Option A is incorrect because 'roles/aiplatform.admin' grants full administrative access, which is too broad for read-only needs. Option B is wrong because exporting the model and sharing via a shared drive bypasses version control and security best practices. Option D is incorrect because Cloud Storage bucket ACLs control access to the underlying bucket, not to the Vertex AI Model Registry; the model is managed through Vertex AI IAM.

Answer

Grant the IAM role 'roles/aiplatform.admin' to the team members.

Answer

Export the model as a local file and share it via a shared drive.

Answer

Add the team members to the Cloud Storage bucket ACL with 'READER' access.

Question 11

Which TWO actions are recommended for collaborating on machine learning models using Vertex AI Model Registry?

Accepted Answer

Use version aliases such as 'champion' and 'challenger' to manage model lifecycle.. Vertex AI Model Registry supports version aliases like 'champion' and 'challenger' to designate which model version should serve as the production candidate and which is under evaluation, enabling controlled lifecycle management and A/B testing without manual version tracking.

Answer

Use Cloud Storage object labels to store model descriptions.

Answer

Deploy all model versions to a single endpoint for comparison.

Answer

Create a separate model entry for each training run.

Question 12

Which TWO strategies help ensure data consistency when multiple teams are contributing features to a shared Vertex AI Feature Store?

Accepted Answer

Define and enforce feature schemas using the Feature Store API.. Defining and enforcing feature schemas using the Vertex AI Feature Store API ensures that all teams adhere to a consistent data structure (e.g., fixed feature names, data types, and value ranges). This prevents schema drift and ingestion conflicts, which are common when multiple teams independently push features to the same feature store. Without schema enforcement, one team might inadvertently change a feature's data type or add unexpected values, breaking downstream models.

Answer

Each team should create their own feature store to avoid conflicts.

Answer

Use only batch ingestion to keep features synchronized.

Answer

Allow each team to independently define feature engineering logic.

Question 13

Which THREE practices improve collaboration when using Cloud Composer for ML pipelines?

Accepted Answer

Use a shared Cloud Storage bucket for intermediate artifacts with appropriate permissions.. Cloud Composer workflows often require sharing intermediate data (e.g., transformed datasets, model checkpoints) across multiple DAGs or team members. A shared Cloud Storage bucket with fine-grained IAM permissions enables secure, centralized artifact exchange without duplicating data or exposing it to unauthorized users. This practice avoids hard-coded paths and ensures that all pipeline stages can reliably access the same artifacts, which is critical for reproducibility and collaboration in ML pipelines.

Answer

Keep all pipeline logic in a single large DAG for simplicity.

Answer

Embed service account keys directly in DAG code for authentication.

Question 14

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.

Answer

Artifact Registry

Answer

Cloud Storage

Answer

Data Catalog

Question 15

A team uses Vertex AI Pipelines. They need to ensure that only certain team members can deploy models to production. What is the best approach?

Accepted Answer

Use IAM roles with custom permissions on the Vertex AI Model Registry. Vertex AI Model Registry supports IAM roles with custom permissions, allowing fine-grained access control over who can promote or deploy models to production. By assigning specific roles (e.g., `roles/aiplatform.modelDeployer`) to only authorized team members, you can restrict deployment actions while still permitting others to view or register models. This approach directly addresses the need to control production deployments without affecting other pipeline stages.

Answer

Use Vertex AI Experiments to track models

Answer

Store model artifacts in a bucket with bucket-level permissions

Answer

Create separate projects for dev and prod