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PDE Practice Question: Your team uses a CI/CD pipeline with Cloud Build…
Your team uses a CI/CD pipeline with Cloud Build to train and deploy ML models on Vertex AI. You want to ensure that only models that pass validation checks (e.g., accuracy threshold, fairness metrics) are promoted to production. What is the best way to implement this?
⚠ Common exam trap
Google Cloud often tests the misconception that Vertex AI Model Registry has built-in automatic promotion based on evaluation metrics, but in reality, it requires external orchestration (like Cloud Build) to implement such logic.
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
✓
In the Cloud Build pipeline, after training, run validation scripts. If validation passes, deploy to a staging endpoint for manual approval, then promote to production.
It integrates validation directly into the CI/CD pipeline using Cloud Build, ensuring that only models passing specific checks (e.g., accuracy threshold, fairness metrics) are promoted. By running validation scripts after training and requiring manual approval before production promotion, this approach provides both automated gatekeeping and human oversight, aligning with MLOps best practices for safe model deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Scheduler to trigger retraining and only deploy if the new model outperforms the previous one on a holdout set.
Why it's wrong here
Cloud Scheduler only triggers jobs on a timetable; it cannot gate promotion on accuracy or fairness thresholds within the pipeline. It is tempting because scheduled retraining is a legitimate MLOps pattern for keeping models fresh, and would be correct if the requirement were periodic retraining rather than validation-gated promotion.
- ✗
Use Vertex AI Model Registry's automatic promotion feature that moves models to production based on evaluation results.
Why it's wrong here
Vertex AI Model Registry stores and versions models but provides no automatic promotion driven by evaluation metrics; promotion is an explicit action. It is tempting because the registry is the natural home for model governance, and would be correct if the requirement were cataloguing and lineage rather than enforcing validation gates.
- ✗
Configure Cloud Functions to re-evaluate the model daily and promote if it passes.
Why it's wrong here
Daily re-evaluation decouples promotion from the CI/CD pipeline, so a model failing accuracy or fairness checks could still reach production between runs. It is tempting because Cloud Functions can call Vertex AI APIs cheaply, and would be correct for asynchronous post-deployment monitoring rather than pre-promotion validation.
- ✓
In the Cloud Build pipeline, after training, run validation scripts. If validation passes, deploy to a staging endpoint for manual approval, then promote to production.
Why this is correct
Validation scripts inside Cloud Build gate promotion on accuracy and fairness metrics, satisfying the stem's requirement that only models passing checks reach production. Staging with manual approval adds human review before promotion, though it introduces delay. This keeps enforcement within the existing pipeline rather than relying on post-deployment monitoring.
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