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PMLE Practice Question: A team is using Vertex AI Pipelines to automate…
A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?
⚠ Common exam trap
Many candidates confuse artifact tracking with model management or deployment features, leading them to select Model Registry or Endpoints instead of recognizing that Experiments provides the run-level metadata and lineage required for reproducibility.
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 Experiments
Vertex AI Experiments is the correct feature because it captures parameters, metrics, and artifacts for each pipeline run, enabling reproducibility and lineage tracking. This directly supports the team's need to ensure runs are reproducible and artifacts are tracked, as Experiments automatically logs metadata for every execution.
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 is for managing ML features.
- ✓
Vertex AI Experiments
Why this is correct
Experiments track parameters, metrics, and artifacts for each run.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model registry tracks model versions but not the full pipeline run.
- ✗
Vertex AI Endpoints
Why it's wrong here
Endpoints are for serving predictions.
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