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

Question 1

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 join ensures that at training time, each row is joined with the feature value as it existed at that specific point in time, preventing leakage from future feature values.

Answer

Online store serving with Bigtable

Answer

Feature store time travel

Answer

Feature monitoring for drift

Question 2

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 captures parameters and metrics automatically when using the Vertex AI SDK or MLflow.

Answer

Vertex AI Prediction

Answer

Vertex AI Workbench

Answer

Vertex AI Metadata

Question 3

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

Answer

Export features to CSV files shared via Cloud Storage

Answer

Each team maintains its own feature engineering code in separate pipelines

Question 4

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

Answer

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

Answer

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

Question 5

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 (ML Metadata) stores and queries lineage information.

Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Question 6

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 support direct Git integration via the user interface, allowing clone, commit, and push operations.

Answer

Use Cloud Source Repositories only

Answer

Use gcloud source repos clone inside the terminal

Answer

Manually download notebooks and upload to GitHub via browser

Question 7

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 designed specifically for versioning large datasets and ML models, working with cloud storage.

Answer

Cloud Storage Object Versioning

Answer

BigQuery table snapshots

Answer

Git LFS

Question 8

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

Answer

Use a larger machine type for Bigtable

Answer

Increase the number of Bigtable nodes

Question 9

What is the primary benefit of using a centralised model registry in MLOps?

Accepted Answer

Governance and version control of models. A centralised model registry provides governance, versioning, and lineage tracking, enabling collaboration and auditability.

Answer

Better hyperparameter tuning

Answer

Faster model training

Answer

Automatic model deployment

Question 10

An ML engineer needs to deploy a model to an endpoint and gradually shift traffic from the previous version (champion) to a new version (challenger) for A/B testing. How should they configure the endpoint?

Accepted Answer

Deploy both versions to the same endpoint and set traffic splitting. Vertex AI endpoints support traffic splitting by assigning percentages to different model versions.

Answer

Use a canary deployment with Cloud Run

Answer

Manually update the endpoint to point to the challenger after testing

Answer

Create a new endpoint for the challenger and route traffic via load balancer

Question 11

A team wants to implement automated model documentation that captures training data, feature importance, evaluation metrics, and intended use. Which Vertex AI feature supports this?

Accepted Answer

Vertex AI Model Registry with model cards. Model cards in Vertex AI Model Registry provide a standardised template for documenting model details.

Answer

Vertex AI Metadata

Answer

Vertex AI Explainable AI

Answer

Vertex AI Pipelines

Question 12

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.

Answer

Use a single endpoint for all models without versioning

Answer

Allow any user with Vertex AI User role to deploy models

Answer

Store all models in Cloud Storage without registry

Question 13

An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?

Accepted Answer

Time travel for accessing previous versions. Delta Lake provides ACID transactions, schema enforcement, and time travel.

Answer

Automatic data encryption at rest

Answer

Built-in real-time streaming

Question 14

A team wants to monitor features in Vertex AI Feature Store for drift. Which TWO configurations are required?

Accepted Answer

Enable feature monitoring on the feature view. Enable feature monitoring on the feature view and configure an alerting channel (e.g., email, Pub/Sub) for drift notifications.

Answer

Set up a BigQuery sink for monitoring results

Answer

Create a Cloud Scheduler job to trigger monitoring

Answer

Deploy a monitoring model to an endpoint

Question 15

Which THREE are valid uses of Vertex AI Metadata?

Accepted Answer

Record the execution of a pipeline step. Vertex AI Metadata tracks lineage of artefacts, executions, and contexts across pipelines.

Answer

Deploy a model to an endpoint

Answer

Store hyperparameter values of an experiment run