easyMultiple Choice
PMLE Practice Question: A machine learning engineer wants to manage…
A machine learning engineer wants to manage multiple model versions and facilitate collaboration across teams. The goal is to track model lineage, versioning, and approvals. Which Vertex AI service should they use?
⚠ Common exam trap
PMLE often tests whether candidates confuse ML Metadata (the underlying lineage store) with Model Registry (the user-facing versioning and approval service), leading them to pick ML Metadata for versioning and approvals.
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 is the service for managing model versions, tracking lineage, and facilitating collaboration and approvals across teams. It provides a central repository where models are registered, versioned, and annotated with metadata, and it integrates with Vertex AI Pipelines and ML Metadata for lineage. This directly matches the requirement to track model lineage, versioning, and approvals.
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 Model Registry
Why this is correct
Vertex AI Model Registry provides centralised versioning, lineage tracking and approval workflows across teams, exactly the governance capabilities the stem requires. It organises model artefacts and their metadata rather than handling training or serving infrastructure.
- ✗
Vertex AI ML Metadata
Why it's wrong here
ML Metadata stores artefacts, executions and lineage, but it is a backend tracking store rather than the interface for managing model versions and approvals. Vertex AI Model Registry is the service that versions models, records lineage and supports approval workflows for collaboration.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves and shares feature values for training and online prediction; it manages feature entities, not model artefacts. Versioning, lineage and approvals for models belong to Vertex AI Model Registry, which is the service the scenario calls for.
- ✗
Vertex AI Vizier
Why it's wrong here
Vizier performs hyperparameter tuning and black-box optimisation, returning suggested trials; it holds no model version history or approval state. Vertex AI Model Registry is the service that versions models, tracks lineage and supports approval workflows across teams.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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