PMLE Collaborating Within and Across Teams to Manage Data and Models Practice Test 5 — 15 Questions

Question 1

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 automatically generates model cards from model metadata, including purpose, evaluation results, and intended use, when models are registered and their metadata is populated. This provides a native, integrated way to document models without manual effort or custom components. It aligns with Vertex AI's governance and documentation features.

Answer

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

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Use Cloud Data Catalog to annotate the model artifact.

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

Question 2

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.

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

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Use the online store with a timestamp filter.

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Create a new feature view with only historical data.

Question 3

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). Feature monitoring (drift detection) is the Vertex AI Feature Store capability that computes drift and skew metrics for feature values against a baseline and emits them for alerting. It is purpose-built to detect when production feature distributions diverge from training or reference distributions. This directly answers the requirement to monitor feature drift.

Answer

Feature views

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Online store

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Point-in-time retrieval

Question 4

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. The queryArtifactLineageSubgraph method returns the lineage subgraph for a given artifact, showing the executions, contexts, and other artifacts connected to it — exactly what is needed to trace which dataset and hyperparameters produced a model. It traverses both upstream (inputs) and downstream (outputs) relationships in the metadata store.

Answer

projects.locations.metadataStores.artifacts.get

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projects.locations.metadataStores.contexts.addContextArtifactsAndExecutions

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projects.locations.metadataStores.executions.queryExecutionInputsAndOutputs

Question 5

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 time travel allows querying previous versions of a Delta table using either a version number or a timestamp, enabling reproducibility for ML experiments and rollback to a prior state. This is the native Delta Lake feature designed for versioning and auditing data changes. It directly satisfies the requirement to version data and roll back.

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Delta Lake streaming

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Delta Lake schema enforcement

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Delta Lake optimization (Z-order)

Question 6

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 (B) is correct because it provides a central repository for managing the lifecycle of ML models, including versioning, tracking model artifacts, and managing deployment to endpoints, which directly satisfies the model versioning and deployment management requirements. Vertex AI Metadata (C) is correct because it captures and stores metadata about artifacts, executions, and contexts, enabling lineage tracking across the ML workflow so teams can trace how models were produced and what data or training runs contributed to them. Together, Model Registry handles versioning and deployment while Metadata provides the lineage and governance context needed for central oversight. Vertex AI Feature Store (A) is for organizing, serving, and sharing feature data, not for model versioning or deployment management. Vertex AI Experiments (D) is for tracking and comparing training experiment runs and metrics, which supports experimentation but not centralized model deployment governance. Cloud Data Catalog (E) is a data discovery and metadata management service for data assets, not a model registry or ML lineage/deployment tool.

Answer

Vertex AI Feature Store

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Vertex AI Experiments

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Cloud Data Catalog

Question 7

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.. Option A is correct because DVC creates small .dvc metafiles (e.g., data.dvc) that record the MD5 hash and path of the tracked data, allowing Git to version the pointer while the actual data lives in the DVC cache. Option B is correct because DVC supports many remote storage backends, including Google Cloud Storage, via `dvc remote add` and `dvc push`, so data can be stored outside the Git repository. Option C is wrong because DVC deliberately keeps large data files out of Git, storing them in its cache and remotes instead. Option D is wrong because DVC supports S3, GCS, Azure Blob Storage, SSH, HDFS, and local remotes, not only S3. Option E is wrong because DVC complements Git for data versioning; it does not replace Git for source code version control.

Answer

DVC stores the actual data files in Git.

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DVC only works with AWS S3 as remote storage.

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DVC replaces Git for code versioning.

Question 8

A team is building a fraud detection model that requires joining real-time transaction features with historical user features. They need to ensure that the training data does not use future information (data leakage). Which Vertex AI Feature Store capability should they use?

Accepted Answer

Point-in-time correct join. Point-in-time correct joins in Vertex AI Feature Store ensure that when training examples are generated, each row uses only feature values that were valid as of the event timestamp of the label — preventing future data from leaking into training. This is the specific capability designed to avoid label leakage in time-series or event-driven ML. Time travel and online serving do not by themselves guarantee temporal correctness of the join.

Answer

Online store serving with Bigtable

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Feature store time travel

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Feature monitoring for drift

Question 9

An ML team wants to automatically track training runs, including hyperparameters and metrics, with minimal code changes. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Experiments with autologging. Vertex AI Experiments with autologging automatically captures parameters, metrics, and artifacts from training runs with minimal code changes—often just a single call to initialize and start a run. This directly satisfies the requirement to track training runs with minimal code. It integrates with the Vertex AI SDK to log framework-specific details (e.g., TensorFlow, PyTorch) automatically.

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Vertex AI Prediction

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Vertex AI Workbench

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Vertex AI Metadata

Question 10

A data science team needs to share features across multiple ML models while ensuring consistency between training and serving. Which approach best achieves this?

Accepted Answer

Use Vertex AI Feature Store to define and serve features for both training and online prediction. Vertex AI Feature Store provides a central repository where features are defined once and reused across models, reducing training-serving skew.

Answer

Store features in a shared BigQuery dataset without versioning

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Export features to CSV files shared via Cloud Storage

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Each team maintains its own feature engineering code in separate pipelines

Question 11

An ML engineer trained a model and registered it in Vertex AI Model Registry. They want to assign the alias 'champion' to the best-performing version for production deployment. Which gcloud command should they use?

Accepted Answer

gcloud ai models versions update --model=MODEL_ID --version=VERSION_ID --update-aliases=champion. The correct command is 'gcloud ai models versions update' with the --update-aliases flag. This command updates a specific model version's aliases, allowing you to assign or remove aliases like 'champion' to denote the best-performing version for production. Option A only describes a version, option B uploads a new model, and option C deploys a model to an endpoint, none of which assign aliases to a version.

Answer

gcloud ai models versions describe --model=MODEL_ID --version=VERSION_ID

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gcloud ai models upload --model-id=MODEL_ID --display-name=champion

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gcloud ai endpoints deploy-model --model=MODEL_ID --alias=champion

Question 12

Which Vertex AI service is used to track the lineage of ML pipeline components, artefacts, and executions?

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata is the service that tracks the lineage of ML pipeline components, artefacts, and executions. It provides a centralized repository to store and manage metadata about ML workflows, enabling reproducibility, auditing, and collaboration. By recording relationships between components, artefacts, and executions, it allows you to trace the provenance of models and data.

Answer

Vertex AI Model Registry

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Vertex AI Feature Store

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Vertex AI Experiments

Question 13

A team uses Vertex AI Workbench managed notebooks. They want to version control their notebook files and collaborate using Git. What is the best way to integrate Git?

Accepted Answer

Use the built-in Git integration in Vertex AI Workbench managed notebooks. Vertex AI Workbench managed notebooks include native Git integration through the JupyterLab Git extension, allowing users to clone repositories, commit, push, and pull directly from the notebook UI without leaving the environment. This is the officially supported and most seamless method for version-controlling notebooks in managed notebooks, supporting GitHub, Cloud Source Repositories, and any Git-compatible remote.

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Use Cloud Source Repositories only

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Use gcloud source repos clone inside the terminal

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Manually download notebooks and upload to GitHub via browser

Question 14

An ML team wants to implement data versioning for large datasets stored in Google Cloud Storage. They need to track changes over time and reproduce previous data states. Which tool is most appropriate?

Accepted Answer

DVC. DVC (Data Version Control) is purpose-built for versioning large datasets and ML models alongside Git. It stores small metadata files in Git while pushing the actual large data to remote storage like Google Cloud Storage, enabling teams to track dataset changes over time and reproduce exact previous data states via commits or tags.

Answer

Cloud Storage Object Versioning

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BigQuery table snapshots

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Git LFS

Question 15

A company uses Vertex AI Feature Store with an online store for low-latency serving. They observe high latency during peak hours. The feature values are small (< 1 KB each) and the workload is read-heavy. Which change would most effectively reduce latency?

Accepted Answer

Switch from Bigtable online store to Optimized online store. Switching from Bigtable online store to Optimized online store is recommended for read-heavy workloads with small feature values, offering lower latency at high QPS.

Answer

Enable caching on the client side

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Use a larger machine type for Bigtable

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Increase the number of Bigtable nodes