PMLE Collaborating Within and Across Teams to Manage Data and Models Practice Test 4 — 15 Questions

Question 1

An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store centralizes feature management, providing an online store for low-latency serving and an offline store for training data retrieval, reducing training-serving skew.

Answer

Cloud Data Catalog

Answer

Vertex AI Model Registry

Answer

Cloud Storage with versioning

Question 2

A data scientist is training a model using Vertex AI Experiments and wants to automatically log model parameters, metrics, and artifacts without modifying their training script. Which approach should they use?

Accepted Answer

Build a custom container with the Vertex AI SDK and MLflow installed, then set the VERTEX_AI_AUTOLOG environment variable to 1.. Vertex AI Experiments supports autologging via the Vertex AI SDK when using MLflow or Keras callbacks. Specifying a custom container with the SDK installed and enabling autologging allows automatic logging without code changes.

Answer

Use the gcloud beta ai experiments autolog command before running the training script.

Answer

Wrap the training code in a Kubeflow Pipelines component that calls the Vertex AI Experiments API.

Answer

Use the Vertex AI Python SDK to create a custom training job and manually log each parameter and metric in the training script.

Question 3

An ML engineer has a model trained in Vertex AI and wants to deploy it to an endpoint with autoscaling and traffic splitting for canary testing. They have the model artifact stored in Vertex AI Model Registry with alias 'champion'. What is the correct sequence of steps?

Accepted Answer

Upload model to registry, create endpoint, then deploy model to endpoint with traffic split.. The standard sequence: upload model to registry, create endpoint, deploy model to endpoint with traffic split.

Answer

Create endpoint, upload model to registry, then deploy model to endpoint with traffic split.

Answer

Create endpoint, deploy model directly from Cloud Storage, then add traffic split.

Answer

Upload model to registry, then create endpoint and deploy in one command using gcloud ai endpoints deploy-model.

Question 4

A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata (part of Vertex ML Metadata) tracks artifacts, executions, and lineage.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Experiments

Question 5

An organization uses Vertex AI Workbench user-managed notebooks and wants to enable collaboration where multiple data scientists can edit the same notebook simultaneously. Which configuration should they use?

Accepted Answer

Use a managed notebook instance (Colab Enterprise) and share the notebook via a link.. Vertex AI Workbench user-managed notebooks do not support real-time collaboration. Managed notebooks (now upgraded to Colab Enterprise) support simultaneous editing via integration with Colab.

Answer

Use Git to branch and merge notebooks, with a CI/CD pipeline to resolve conflicts.

Answer

Share the user-managed notebook instance by giving multiple users IAM roles to access the same JupyterLab instance.

Answer

Store the notebook in Cloud Storage and ask users to edit sequentially using gsutil cp.

Question 6

A company uses BigQuery as their data warehouse. They want to version datasets for ML experiments and be able to query snapshots at specific points in time. Which approach is most cost-effective and requires minimal operational overhead?

Accepted Answer

Create BigQuery table snapshots at key milestones.. BigQuery table snapshots are a built-in feature for point-in-time recovery and versioning at low cost (only storage changes).

Answer

Use BigQuery table clones to create copies of the data.

Answer

Export the table to Cloud Storage as Parquet and use DVC to version the files.

Answer

Use BigQuery time-travel (7-day window) to query historical data without snapshots.

Question 7

An ML team uses Vertex AI Pipelines and wants to automatically generate model cards documenting model purpose, evaluation results, and intended use. Which approach should they take?

Accepted Answer

Use Vertex AI Model Registry to generate model cards automatically from model metadata.. Vertex AI Model Registry supports model cards that can be populated programmatically via the SDK or Cloud Console.

Answer

Manually create a Google Doc and share it with the team.

Answer

Use Cloud Data Catalog to annotate the model artifact.

Answer

Write a custom Kubeflow Pipelines component that creates a BigQuery table with model metadata.

Question 8

A team is using Vertex AI Feature Store with an online store for low-latency serving. They notice increasing latency during peak hours. The feature data is updated frequently and requires strong consistency. Which online store type should they use?

Accepted Answer

Bigtable online store. Bigtable online store is recommended for high-throughput, low-latency, and strong consistency requirements.

Answer

Optimized online store

Answer

Cloud Spanner online store

Answer

Firestore online store

Question 9

A data scientist needs to retrieve training data from Vertex AI Feature Store that exactly matches the feature values as they were at a specific historical timestamp to avoid label leakage. Which feature view configuration should they use?

Accepted Answer

Enable point-in-time retrieval on the feature view.. Point-in-time retrieval is a feature of Vertex AI Feature Store that returns feature values as of a specified timestamp.

Answer

Use the offline store without point-in-time and rely on data ordering.

Answer

Use the online store with a timestamp filter.

Answer

Create a new feature view with only historical data.

Question 10

An ML team wants to monitor feature drift in their production model. Which Vertex AI Feature Store capability should they use?

Accepted Answer

Feature monitoring (drift detection). Vertex AI Feature Store includes feature monitoring that can detect drift between training and serving data.

Answer

Feature views

Answer

Online store

Answer

Point-in-time retrieval

Question 11

A company uses Vertex AI Pipelines to orchestrate ML workflows. After a pipeline run, they want to query the lineage of a particular model artifact to find out which dataset and hyperparameters were used to produce it. Which API method should they use?

Accepted Answer

projects.locations.metadataStores.artifacts.queryArtifactLineageSubgraph. Vertex AI Metadata allows querying lineage via the lineagesubgraph method to retrieve upstream and downstream artifacts.

Answer

projects.locations.metadataStores.artifacts.get

Answer

projects.locations.metadataStores.contexts.addContextArtifactsAndExecutions

Answer

projects.locations.metadataStores.executions.queryExecutionInputsAndOutputs

Question 12

A team is using Delta Lake on Dataproc for their data lake with ACID transactions. They want to version data for ML experiments and roll back to a previous version if needed. Which Delta Lake feature should they use?

Accepted Answer

Delta Lake time travel. Delta Lake provides time travel via VERSION AS OF or TIMESTAMP AS OF.

Answer

Delta Lake streaming

Answer

Delta Lake schema enforcement

Answer

Delta Lake optimization (Z-order)

Question 13

An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry manages model versions and aliases; Vertex AI Metadata tracks lineage.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Cloud Data Catalog

Question 14

An ML team uses Vertex AI Workbench managed notebooks and wants to version their notebook code and collaborate using Git. Which THREE steps are required to set up Git integration? (Select 3)

Accepted Answer

Configure Git credentials (username/email) using git config.. To use Git in managed notebooks, you need to install git, configure credentials, and clone the repository.

Answer

Create a Cloud Source Repositories mirror of the GitHub repo.

Answer

Enable the Vertex AI Notebooks API for Git sync.

Question 15

A company wants to use DVC for data versioning alongside their ML code in Git. Which TWO statements about DVC are correct? (Select 2)

Accepted Answer

DVC uses a separate .dvc file to track data versions.. DVC tracks data files by storing their hashes in Git and uses a remote storage for the actual data. It can integrate with cloud storage like GCS.

Answer

DVC stores the actual data files in Git.

Answer

DVC only works with AWS S3 as remote storage.

Answer

DVC replaces Git for code versioning.