PMLE Collaborating to manage data and models Practice Question
Which THREE of the following are recommended practices for model governance and lineage in Vertex AI?
⚠ Common exam trap
Google Cloud often tests the distinction between using native Vertex AI services (like ML Metadata, Experiments, and Model Registry) versus ad-hoc or manual methods (like spreadsheets or custom databases) that lack automated governance and audit trails.
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
✓
Enable Vertex AI ML Metadata to track artifacts, executions, and contexts.
Vertex AI ML Metadata is a fully managed service that automatically tracks artifacts, executions, and contexts across the ML workflow. By enabling it, you create a lineage graph that records every step from data preparation to model deployment, which is essential for auditability and reproducibility. This is a core recommended practice for model governance because it provides an immutable, queryable history of all model-related activities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Vertex AI ML Metadata to track artifacts, executions, and contexts.
Why this is correct
ML Metadata provides automated lineage tracking.
- ✓
Use Vertex AI Experiments to log parameters and metrics.
Why this is correct
Experiments capture run metadata for comparison.
- ✗
Store model artifacts in Cloud Storage with metadata in a database.
Why it's wrong here
While possible, this is disjointed; Vertex AI provides integrated tools.
- ✗
Manually record model lineage in a spreadsheet.
Why it's wrong here
Manual records are error-prone and not auditable.
- ✓
Use Vertex AI Model Registry to manage model versions and stages.
Why this is correct
Model Registry enables version control and promotion workflows.
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