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

A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

⚠ Common exam trap

Candidates often confuse Vertex AI's data/experiment tracking services (Feature Store, Experiments, Metadata) with the model governance service (Model Registry) — candidates often pick Metadata because it sounds like the 'registry' layer.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Vertex AI Model Registry

Vertex AI Model Registry (B) is the centralized repository for managing the lifecycle of ML models, providing a single source of truth for governance across an organization, which directly matches the requirement for a centralized model registry. Model versioning and aliases (D) are core capabilities of the Model Registry that let teams track multiple model versions, assign meaningful aliases (e.g., 'production', 'staging'), and control which version is deployed, which is essential for governance and reproducibility. Together, B and D provide the registry plus the version/alias controls needed for centralized model governance. Vertex AI Feature Store (A) manages feature storage and serving, not model registration, so it does not fulfill the model registry requirement. Vertex AI Experiments (C) tracks experiment runs, parameters, and metrics for training, not model governance or registration. Vertex AI Metadata (E) stores metadata about artifacts and executions in a lineage graph, but it is not the centralized model registry itself.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves, versions and monitors feature values for training and online inference; it does not register trained models, so it cannot centralise model governance. It is tempting because it is the governance hub for features, and would be correct if the requirement were feature reuse and consistency across pipelines.
✓
Vertex AI Model Registry
Why this is correct
Vertex AI Model Registry is the centralised catalogue that tracks model artefacts, versions and lineage across projects. It satisfies the governance requirement by giving one authoritative location to register, discover and manage models throughout their lifecycle.
✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks and compares training runs, metrics, and parameters; it does not store, version, or govern model artefacts. It is the right choice for experiment tracking and reproducibility, whereas a model registry handles lineage, approval, and deployment governance.
✓
Model versioning and aliases
Why this is correct
Model versioning and aliases provide the traceability and controlled promotion that centralised governance demands. Aliases let teams reference a stable name, such as "production", while versions remain immutable, so Microsoft Entra ID-governed access and audit trails apply consistently across the registry without retraining pipelines whenever a new model version is registered.
✗
Vertex AI Metadata
Why it's wrong here
Metadata stores artefacts, lineage and metrics for tracking experiments, not versioned model registration with approval states. It is tempting because it underpins Vertex AI's governance tooling, but a registry requires the Model Registry's versioning and alias assignment, which Metadata alone does not provide.

Go deeper

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

Why each option matters

Option-by-option breakdown

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