PMLE Collaborating to manage data and models Practice Question
A data science team is using Vertex AI Pipelines to orchestrate their ML workflows. They want to ensure that each pipeline run is reproducible and that artifacts are versioned. Which Vertex AI feature should they use to track and manage pipeline artifacts?
⚠ Common exam trap
Watch out — candidates often confuse Vertex AI Experiments with Vertex ML Metadata; while both track metadata, only Vertex ML Metadata provides comprehensive artifact lineage and versioning for pipeline executions.
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 dedicated service for capturing and managing metadata and artifacts from Vertex AI Pipelines. It records executions, artifacts, and their relationships, enabling lineage tracking and reproducibility. The other options serve different purposes: Experiments for run comparison, Model Registry for models, and Feature Store for feature serving.
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 Model Registry
Why it's wrong here
Vertex AI Model Registry manages model versions and their metadata, but it is focused on models rather than all pipeline artifacts like datasets or trained models. It does not capture the full lineage of pipeline executions or intermediate artifacts, so it is not suitable for tracking all artifacts produced by a pipeline.
- ✓
Vertex ML Metadata
Why this is correct
Vertex ML Metadata automatically tracks artifacts, executions, and contexts produced by Vertex AI Pipelines runs. It provides lineage and versioning, enabling reproducibility by recording parameters and artifacts. This is the native service for managing pipeline artifacts and their relationships, making it the correct choice for the team's requirement.
- ✗
Vertex AI Experiments
Why it's wrong here
Vertex AI Experiments is designed to track and compare experiment runs, including metrics and parameters, but it does not manage pipeline artifacts or their lineage. While it can log pipeline runs, it lacks the artifact versioning and dependency tracking that Vertex ML Metadata provides. Thus, it does not fully meet the requirement.
- ✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store is used for storing and serving features for online and offline predictions. It does not track pipeline artifacts or provide lineage for ML workflows. Using it for artifact management would be inappropriate and would not satisfy the need for reproducibility and versioning of pipeline outputs.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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