PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A team uses Vertex AI Pipelines and wants to track lineage of artifacts and executions. Which three resources should they use? (Choose three.)
⚠ Common exam trap
PMLE often tests the distinction between Metadata (lineage: artifacts/executions) and Experiments (runs/metrics) — candidates pick Experiments because it sounds like the tracking service.
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
✓
Artifacts
Vertex AI Metadata is the core lineage service that stores and connects metadata about ML resources, so option C is correct because it provides the underlying metadata store for tracking lineage. Within that metadata store, Artifacts (option A) represent the inputs and outputs of pipeline steps—such as datasets, models, and metrics—and are the nodes whose lineage is tracked. Executions (option E) represent a single run of a pipeline step or component and record the events that consume and produce artifacts, which is exactly what links artifacts together into a lineage graph. Vertex AI Experiments (option B) is for tracking and comparing experiment runs and metrics, not for artifact/execution lineage, and Model Registry (option D) is for managing model versions and deployment, not for general lineage tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Artifacts
Why this is correct
Artifacts are the versioned, typed outputs and inputs that Vertex AI ML Metadata records, such as datasets, models and metrics. Naming them satisfies the lineage requirement because each Artifact node links to the Executions and Contexts that produced or consumed it, forming the traceable graph.
- ✗
Vertex AI Experiments
Why it's wrong here
Experiments record runs, metrics and parameters for model training comparison, not pipeline artifact and execution lineage. Lineage requires Vertex ML Metadata, whose Artifacts, Executions and Contexts graph the relationships the team needs. Experiments would be the right choice for tracking and comparing training run metrics across iterations.
- ✓
Vertex AI Metadata
Why this is correct
Vertex AI Metadata provides the lineage backbone: artefacts, executions and contexts are recorded as nodes and edges in the metadata store, with events linking them. It satisfies the stem's tracking requirement directly, since every pipeline run writes execution and artefact records that can be queried for provenance.
- ✗
Model Registry
Why it's wrong here
Model Registry catalogues trained models, versions and their deployment aliases; it does not record pipeline artifact or execution lineage. Vertex ML Metadata stores that lineage graph. Model Registry would be correct when the goal is organising, versioning and promoting models through deployment stages.
- ✓
Executions
Why this is correct
Executions record each pipeline run as a lineage node, capturing parameters, start and end times, and the artifacts consumed and produced. This satisfies the stem's requirement to track execution lineage alongside artifacts, letting Vertex AI ML Metadata link runs to their inputs and outputs for full provenance.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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