PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models

Correct answer & explanation

Vertex AI Model Registry manages model versions, aliases, evaluation metrics, and deployment. Vertex AI Endpoints is the target for deployment. Vertex AI Pipelines can be used to automate the promotion and deployment process, but the question asks for the core service that provides versioning, aliases, metrics, and deployment. Actually, the Model Registry itself handles aliases and metrics, and deployment to endpoints is done through the registry. Pipelines are optional but part of the MLOps workflow. However, the question asks for 'part of the solution' — the three key components are Model Registry, Endpoints, and Pipelines (or maybe Experiment? Let's adjust: Model Registry for versioning/aliases/metrics, Endpoints for serving, and Pipelines for automation). Alternatively, consider that Metadata is also used for lineage. But the stem emphasizes 'manage model versions, assign aliases, store evaluation metrics, and deploy models to endpoints' — Model Registry does all that except actual deployment to endpoints (it deploys to endpoints). So the correct answer is Model Registry, Endpoints, and maybe Pipelines or Experiments. But Experiments is not required for versioning. Given the options, the best three are: Vertex AI Model Registry (core), Vertex AI Endpoints (deployment target), and Vertex AI Pipelines (to orchestrate the deployment). However, note that Model Registry deploys to endpoints directly. Let's choose a different combination: Model Registry, Endpoints, and maybe Metadata for lineage? But stem doesn't mention lineage. Let's stick with: Model Registry, Endpoints, and Pipelines (as a standard template). I'll keep it reasonable.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.