PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?
⚠ Common exam trap
PMLE often tests the confusion between Vertex AI Experiments (run tracking and comparison) and Vertex ML Metadata (lineage and artifact relationships), causing candidates to pick Experiments when lineage is the requirement.
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 (part of Vertex ML Metadata) is the service designed to record and query ML metadata, including artifacts (datasets, models), executions (pipeline runs), and events, forming a lineage graph. It captures which datasets, parameters, and models were used in each pipeline execution, enabling reproducibility and auditability. This directly matches the requirement to track lineage of ML pipeline runs.
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 Metadata
Why this is correct
Vertex AI Metadata stores artefacts and executions in a lineage graph, recording which datasets, parameters and models each pipeline run consumed and produced. This directly satisfies the requirement to track lineage across ML pipeline executions.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves and manages feature values for training and online serving; it does not record pipeline run lineage across datasets, parameters and models. It is tempting because it tracks feature provenance, which suits feature reuse scenarios, but run-level lineage metadata belongs to Vertex AI Experiments.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry catalogues and versions trained models, providing model lineage only; it does not record which datasets and parameters each pipeline run used. It is tempting because it tracks model provenance, which suits deployment governance, but end-to-end run lineage needs Vertex ML Metadata.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments tracks and compares experiment runs, logging parameters and metrics, but it does not capture full artefact lineage linking datasets and models across pipeline executions. It is tempting because it records run metadata, which fits model tuning comparisons, yet pipeline lineage requires Vertex ML Metadata.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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