PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?
⚠ Common exam trap
PMLE often tests the confusion between Experiments (which run produced these metrics) and Metadata (which artifacts depend on which), so candidates choose Experiments when the question asks for dependency traversal.
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 Metadata
Vertex AI Metadata stores the ML metadata graph produced by Vertex AI Pipelines, including artifacts (datasets, models, metrics), executions, and their input/output relationships. It exposes APIs to traverse this graph in both directions, so you can query upstream sources and downstream dependents of any artifact. This is the correct service for cross-run lineage queries.
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 serves and shares engineered feature values for online and offline training, not pipeline artifact relationships. It records no dataset-to-model lineage, so dependency queries fail. It would be correct when features must be reused consistently between training and serving.
- ✗
Vertex AI Experiments
Why it's wrong here
Experiments records metrics and parameters for comparing training runs, not artifact dependency graphs. It cannot answer upstream or downstream queries because it stores no lineage edges between datasets, models and metrics. It would be the right choice for ranking and comparing run performance during model selection.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry catalogues model versions, their deployment state and associated metadata, but holds no dataset or metric lineage edges. It cannot traverse upstream or downstream dependencies. It would be correct for promoting, versioning and rolling back deployed models across environments.
- ✓
Vertex AI Metadata
Why this is correct
Vertex AI Metadata stores pipeline resources as a lineage graph of executions, artifacts and contexts, so you can query upstream and downstream dependencies of any artifact. Cloud Logging records events but holds no typed lineage relationships between datasets, models and metrics.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.