PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
Your organization uses Vertex AI Pipelines for training. A compliance auditor asks you to prove which dataset version and which preprocessing code commit produced a model that is currently deployed. You need to retrieve this information programmatically for a specific model version. Which approach should you use?
⚠ Common exam trap
The trap here is trusting a human-readable description or display name instead of the machine-recorded lineage graph.
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
✓
Use Vertex ML Metadata to traverse the lineage from the model artifact to its parent execution and input artifacts.
Vertex ML Metadata is the system of record for lineage, linking artifacts such as models and datasets through executions. Traversing lineages from the model artifact to its parent execution and input artifacts yields the dataset version and code commit programmatically, which is exactly what an auditor needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
List the endpoint's deployed models and read the model description field, which automatically records the dataset version and code commit.
Why it's wrong here
The model description field is free text that must be populated by a human; it is not automatically filled with dataset or code provenance. Reading it would give whatever someone typed, not a verifiable chain of lineage, so it cannot satisfy an auditor's requirement for proof.
- ✓
Use Vertex ML Metadata to traverse the lineage from the model artifact to its parent execution and input artifacts.
Why this is correct
Vertex ML Metadata stores artifacts, executions, and events, so you can start from the model artifact and walk backward to the training execution, then to the dataset artifact and code artifact that were inputs. This provides an auditable, programmatic chain of provenance for the deployed model version.
- ✗
Inspect the model's Cloud Storage directory for a metadata.json file that lists the dataset and code commit.
Why it's wrong here
Vertex AI does not write a metadata.json file into the model artifact directory that lists dataset and code provenance. Any such file would have to be created manually and could be missing or inaccurate. Relying on it is not an auditable mechanism and would likely fail the compliance requirement.
- ✗
Query Vertex AI Experiments for the run that has the same display name as the model version.
Why it's wrong here
Display names are not guaranteed unique and are not the canonical link between a model version and its training run. Matching by name is fragile and can return the wrong run, so an auditor cannot rely on it as proof of provenance. The correct approach uses the model version's lineage relationships instead.
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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
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