hardMultiple Choice
PMLE Practice Question: Maintain an audit trail of model changes for…
A company needs to maintain an audit trail of model changes for compliance. Multiple teams will be updating models. What is the best approach to track who created, modified, or deployed each model version?
⚠ Common exam trap
PMLE often tests whether candidates confuse generic logging (Cloud Logging, Cloud Storage audit logs) with purpose-built ML lineage (Vertex AI Metadata) — the trap is picking a logging answer that lacks model-version context.
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 AI Experiments and Metadata to track model lineage and audit logs
Vertex AI Experiments and Metadata provide built-in lineage tracking that records which experiment, run, artifact, and model version were created, by which user, and with which parameters and metrics. Combined with Cloud Audit Logs for Vertex AI API calls, this gives a complete, queryable audit trail of who created, modified, or deployed each model version — exactly what compliance requires.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Cloud Storage audit logs and require all model files to be stored in a bucket
Why it's wrong here
Cloud Storage audit logs capture bucket-level object access, not model lifecycle events such as creation, modification or deployment. They suit detecting unauthorised reads of stored objects; tracking version authorship requires a model registry that records those operations directly.
- ✗
Use Cloud Logging to collect logs from all services and search for model names
Why it's wrong here
Cloud Logging records service-level events and lacks model-version lineage, so correlating who created or deployed a version is unreliable. It suits general operational troubleshooting; a dedicated model registry, which versions artefacts and records authorship, is required for compliance audit trails.
- ✓
Use Vertex AI Experiments and Metadata to track model lineage and audit logs
Why this is correct
Vertex AI Experiments and Metadata record lineage automatically, capturing parameters, artifacts and the identity behind each run. This satisfies the compliance requirement for an audit trail showing who created, modified or deployed every model version across multiple teams.
- ✗
Ask team members to maintain a shared spreadsheet of changes
Why it's wrong here
A shared spreadsheet relies on manual entry, so it cannot capture model creation, modification or deployment events automatically, and offers no tamper-evident audit trail. Spreadsheets suit ad-hoc team tracking of non-critical items, but compliance auditing requires the platform's built-in model versioning and activity logging, which records each actor and action.
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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.