PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?
⚠ Common exam trap
PMLE often tests the overlap between Vertex AI Experiments (run tracking) and Vertex AI Metadata (lineage graph), so candidates pick Experiments when the question explicitly asks for upstream/downstream dependency queries.
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 Metadata
Vertex AI Metadata is the service that records and stores ML metadata — artifacts, executions, and contexts — and their relationships, forming the lineage graph. It lets you query upstream and downstream dependencies of any artifact, such as finding all models and metrics derived from a dataset. This is exactly the lineage-query capability the team needs.
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 and manages feature values for training and serving, not artifact lineage. It is tempting because it tracks feature provenance, but it would be correct when the requirement is reusing consistent features across models rather than tracing downstream dataset dependencies.
- ✗
Vertex AI Experiments
Why it's wrong here
Experiments records runs, parameters and metrics for comparison, not the dependency graph between artifacts. It is tempting because it captures pipeline metadata, but it would be correct when the requirement is comparing training runs rather than querying downstream lineage.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry tracks model versions and their deployment metadata, not dataset-to-artifact dependency graphs. It is tempting because it stores model lineage, but it would be correct when the requirement is registering, versioning and promoting models rather than querying dataset lineage.
- ✓
Vertex AI Metadata
Why this is correct
Vertex AI Metadata stores artefacts, executions and contexts in a lineage graph, so querying it returns every downstream artefact derived from a given dataset. This satisfies the stem's need to trace dependencies from a specific dataset artefact.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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