easyMultiple Select
PDE Practice Question: Designing a CI/CD pipeline for their ML models…
A company is designing a CI/CD pipeline for their ML models using Cloud Build and Vertex AI. Which TWO practices should they adopt to ensure reliable and reproducible deployments?
⚠ Common exam trap
Google often tests the misconception that manual approval gates or single-bucket storage without versioning are acceptable for reproducibility, when in fact they undermine automation and traceability in CI/CD pipelines.
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 immutable container images with version tags for each model deployment
Option C is correct because using immutable container images with version tags ensures that each model deployment is tied to a specific, unchangeable artifact, which is essential for reproducibility and rollback in a CI/CD pipeline. Option D is correct because including unit tests for data preprocessing and feature engineering code in the pipeline catches errors early and verifies that the transformation logic behaves consistently, which directly supports reliable and reproducible ML deployments. Option A is not required for reproducibility and would slow the pipeline; manual approval is a governance choice, not a technical practice for reliable, reproducible deployment. Option B is wrong because storing artifacts in a single bucket without versioning prevents tracking and reproducing specific model versions. Option E is wrong because deploying every model version directly to production bypasses validation and testing, undermining reliability and reproducibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Require manual approval for every model change before deployment
Why it's wrong here
Manual approval gates introduce human latency and inconsistency, undermining the automated reproducibility a CI/CD pipeline requires; reproducibility comes from versioned, automated build and deployment steps. It is tempting because approval gates suit regulated release governance, but they do not make deployments reproducible.
- ✗
Store all model artifacts in a single Cloud Storage bucket without versioning
Why it's wrong here
A single bucket without object versioning overwrites or loses prior model artefacts, so a pipeline cannot retrieve the exact model version tied to a build, breaking reproducibility. It is tempting because one bucket is simple storage, but versioning is what preserves immutable artefact lineage.
- ✓
Use immutable container images with version tags for each model deployment
Why this is correct
Immutable, version-tagged container images pin the exact code and dependencies used at training and serving time. This eliminates drift between pipeline runs, giving the reproducible deployments the stem requires, since a tag always resolves to identical, unmodified image content.
- ✓
Include unit tests for data preprocessing and feature engineering code in the pipeline
Why this is correct
Unit tests over preprocessing and feature engineering code catch transformation bugs before deployment, ensuring training and serving compute features identically. This guards against silent data errors that would otherwise break reproducibility and reliability across pipeline runs.
- ✗
Deploy every model version directly to production for immediate use
Why it's wrong here
Deploying every version straight to production removes staging validation and rollback safety, so untested models reach users and reproducibility cannot be verified. It is tempting because it maximises release speed, but progressive rollout via Vertex AI endpoints is what supports safe, repeatable deployment.
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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.