PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
Which Vertex AI service is used to track the lineage of ML pipeline components, artefacts, and executions?
⚠ Common exam trap
PMLE often tests the distinction between Vertex AI services that sound similar, such as confusing Metadata with Experiments or Model Registry, because all deal with tracking aspects of ML workflows.
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 tracks the lineage of ML pipeline components, artefacts, and executions. It provides a centralized repository to store and manage metadata about ML workflows, enabling reproducibility, auditing, and collaboration. By recording relationships between components, artefacts, and executions, it allows you to trace the provenance of models and data.
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 provides the lineage tracking required, storing artefacts, executions and contexts as nodes within a managed metadata graph. It records relationships between pipeline components and their outputs, satisfying the stem's demand for tracing component, artefact and execution provenance across ML workflows.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry catalogues and versions trained models, tracking their deployment stage, not the lineage of pipeline components, artefacts and executions. It is tempting because it does record model provenance, and would be correct when promoting, approving or rolling back model versions across environments.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves and manages feature values for training and online prediction; it holds no lineage records for components, artefacts or executions. It is tempting because it centralises ML metadata, and would be correct when the requirement is consistent feature reuse across training and serving.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments records runs, parameters and metrics for comparing training trials; it does not map component, artefact and execution lineage. It is tempting because it tracks ML metadata, and would be correct when comparing hyperparameter runs to select the highest-performing model.
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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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