PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
A data scientist wants to track machine learning experiments, including parameters, metrics, and artifacts, and compare runs. Which Vertex AI service should they use?
⚠ Common exam trap
PMLE often tests the overlap between Vertex AI Experiments and Vertex AI Metadata — candidates may pick Metadata, but Experiments is the user-facing service for tracking and comparing runs.
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 service designed to track ML experiments, including parameters, metrics, and artifacts, and to compare runs. It integrates with Vertex AI Metadata to log experiment lineage and provides a UI for comparing runs. This directly matches the data scientist's requirement.
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 it's wrong here
Metadata stores lineage and artifact records for governance, but it is not the experiment-tracking interface for logging parameters, metrics and comparing runs. It is tempting because it underlies Vertex AI Experiments, and would be correct when auditing dataset and model lineage across pipelines rather than comparing training runs.
- ✓
Vertex AI Experiments
Why this is correct
Vertex AI Experiments logs parameters, metrics, and artifacts for each run and provides comparison across runs within a experiment. This directly satisfies the need to track training metadata and evaluate multiple runs side by side.
- ✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store manages feature data for training and serving, but it lacks the capability to log experiment parameters, metrics, or artifacts, nor does it provide run comparison views—those functions belong to Vertex AI Experiments. It is tempting because feature stores are integral to ML workflows, and one might assume they track experiment metadata; however, Feature Store is correctly chosen when the need is to centralise and serve consistent feature values across training and online prediction, not to record and compare individual experiment runs.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry versions and deploys trained models; it records no per-run parameters, metrics or artifacts, so run comparison is impossible. It is tempting because it does catalogue ML assets, and it would be right when promoting a model through deployment stages after experimentation has finished.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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