hardMultiple Select
PDE Practice Question: A data science team uses Cloud Build and Vertex…
A data science team uses Cloud Build and Vertex AI to implement CI/CD for their machine learning models. Which THREE steps are essential for a production-ready operationalization pipeline? (Choose 3.)
⚠ Common exam trap
Google Cloud often tests the misconception that full automation (Option C) is always better, but the trap here is that production-ready pipelines require human-in-the-loop approval for critical model changes to ensure accountability and safety.
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
✓
Deploy the model to a staging endpoint for manual approval before promoting to production.
Option B is correct because a production-ready operationalization pipeline should gate promotion with a staging endpoint and manual approval, so a human can verify the model behaves correctly before it serves live traffic. Option D is correct because Vertex AI Model Evaluation compares the candidate model's metrics against the current production model, ensuring the new version actually improves or meets quality thresholds before promotion. Option E is correct because embedding unit and integration tests for the training code in the Cloud Build pipeline catches data, feature, and code regressions early in CI/CD, which is essential for reliable automated ML delivery. Option A is not correct because storing training artifacts in Cloud Storage without versioning breaks reproducibility and rollback, which are required for production ML pipelines. Option C is not correct because automatically deploying every new model version straight to production bypasses validation and approval, risking unvetted or degraded models serving users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store all training artifacts in Cloud Storage without versioning.
Why it's wrong here
Unversioned Cloud Storage buckets break reproducibility: a pipeline cannot roll back or trace which artefact produced a model, defeating Vertex AI lineage. Versioning is tempting to skip for speed, but object versioning is exactly the control that makes artefact retrieval deterministic; it would be acceptable only for disposable scratch data.
- ✓
Deploy the model to a staging endpoint for manual approval before promoting to production.
Why this is correct
A staging endpoint with manual approval inserts a human gate between model build and production release, satisfying the governance constraint that unvalidated models must not reach live traffic. Vertex AI supports this via endpoint deployment and traffic splitting, enabling controlled promotion after sign-off.
- ✗
Automatically deploy every new model version directly to the production endpoint.
Why it's wrong here
Auto-deploying every version to production bypasses the evaluation and approval gate, so an underperforming model reaches live traffic unchecked. Continuous deployment is genuinely useful for staging or canary environments, where automated rollout is the goal; production endpoints require a promotion decision after validation metrics pass.
- ✓
Use Vertex AI Model Evaluation to validate the new model against the current production model metrics.
Why this is correct
Vertex AI Model Evaluation compares the candidate model's metrics against the incumbent production model, satisfying the requirement to prevent performance regression before promotion. This quantitative gate is what distinguishes a production-ready pipeline from one that deploys blindly on every successful build.
- ✓
Include unit and integration tests for the training code in the Cloud Build pipeline.
Why this is correct
Unit and integration tests in Cloud Build verify training code correctness before any model artefact is built or deployed, satisfying the pipeline's quality-gate requirement. Catching code defects at build time prevents flawed training runs from consuming Vertex AI resources and producing unusable models.
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Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.