PMLE Collaborating to manage data and models Practice Question
A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?
⚠ Common exam trap
It's easy for candidates to confuse Vertex AI Experiments (which tracks run metrics and parameters) with lineage tracking, but Experiments does not capture the full artifact-to-execution graph that ML Metadata provides for dataset provenance.
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 ML Metadata
Vertex ML Metadata is the correct choice because it is specifically designed to track the lineage of datasets, models, and other artifacts throughout the ML lifecycle. It records metadata about each step in a pipeline, including the source dataset used for a training run, enabling full provenance tracking. This allows data scientists to trace back which data was used, how it was transformed, and which model version it produced.
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 ML Metadata
Why this is correct
Vertex ML Metadata records artefacts, executions and contexts, automatically capturing dataset inputs and training runs as lineage graphs. This directly satisfies the stem's requirement to track which dataset fed a training run, letting the scientist query provenance rather than reconstruct it manually from logs.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves and reuses engineered feature values for training and serving; it does not record which dataset version a run consumed. It is tempting because it manages training data centrally, but lineage tracking is provided by Vertex AI Lineage, not the feature repository.
- ✗
Vertex AI Experiments
Why it's wrong here
Experiments records runs, parameters and metrics for comparison, but does not itself capture dataset lineage. It is tempting because experiments log training-run metadata, yet the lineage requirement is met by Vertex AI Lineage, which tracks dataset and artefact provenance.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry catalogues trained model versions and their metadata, not the datasets feeding training runs. It is tempting because it tracks artefacts and versions, but dataset lineage belongs to Vertex AI Lineage, which records dataset-to-run relationships in the metadata store.
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