easyMultiple Choice
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
Vertex AI Feature Store centralises feature serving and training/serving consistency, but it does not record pipeline run parameters or artifact lineage. Vertex ML Metadata tracks those executions and artifacts. Feature Store is correct when the requirement is reusing engineered features across models without training-serving skew.
- ✓
Vertex AI Experiments
Why this is correct
Vertex AI Experiments records parameters, metrics and artefacts for each pipeline run, giving lineage and comparison across executions. This satisfies the reproducibility and artefact-tracking requirement, unlike raw pipeline execution alone, which does not persist experiment-level metadata.
- ✗
Vertex AI Model Registry
Why it's wrong here
Vertex AI Model Registry catalogues and versions trained models, but it does not record pipeline parameters, component lineage or artifact provenance across runs. Vertex ML Metadata provides that tracking and reproducibility. Model Registry is the right choice when the goal is governing model versions and deployment stages.
- ✗
Vertex AI Endpoints
Why it's wrong here
Vertex AI Endpoints serve trained models for online prediction; they hold no record of pipeline parameters, component executions or artifact lineage, so reproducibility is unaddressed. Vertex ML Metadata captures that lineage. Endpoints are correct when the requirement is deploying a model for low-latency inference.
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