mediumMultiple Select
PMLE Practice Question: Which THREE are best practices for implementing…
Which THREE are best practices for implementing CI/CD for ML pipelines on Google Cloud? (Choose THREE.)
⚠ Common exam trap
Google Cloud often tests the distinction between general software CI/CD practices and ML-specific CI/CD needs, trapping candidates who over-apply traditional unit testing or assume low-code tools are always best practices for production ML 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
✓
Maintain separate environments for dev, staging, and production
Option A is correct because maintaining separate dev, staging, and production environments isolates changes and lets you validate ML pipeline updates on representative data before promoting them to production, which is a core CI/CD practice on Google Cloud. Option B is correct because Vertex ML Metadata records experiments, parameters, metrics, and artifacts, giving the lineage and reproducibility needed to compare runs and roll back or promote models reliably. Option C is correct because Cloud Build is Google Cloud's managed CI/CD service and can automate testing, building, and deploying pipeline components (for example, via cloudbuild.yaml and Vertex AI Pipelines), which is exactly the automation CI/CD requires. Option D is not a CI/CD best practice per se; low-code components may speed development but do not address continuous integration, delivery, or reproducibility. Option E is too narrow and absolute: unit tests for training jobs are useful, but writing them for every training job is not one of the three defining best practices for CI/CD on Google Cloud.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Maintain separate environments for dev, staging, and production
Why this is correct
Separate dev, staging, and production environments isolate pipeline changes so untested components never reach production data or endpoints. This satisfies CI/CD best practice by enabling promotion gates, reproducible validation, and rollback, preventing a faulty model or pipeline from affecting live traffic.
- ✓
Track all experiments and artifacts using Vertex ML Metadata
Why this is correct
Vertex ML Metadata records experiments, parameters, metrics, and artefacts, giving lineage across pipeline runs. This satisfies CI/CD traceability, letting teams compare model versions, reproduce results, and audit which data and code produced a deployed artefact before promotion.
- ✓
Use Cloud Build to automate testing, building, and deployment of pipeline components
Why this is correct
Cloud Build provides managed CI/CD execution that automates testing, container building, and pipeline deployment on commit. This satisfies the automation requirement, ensuring components are validated and registered consistently rather than deployed manually, which reduces human error across environments.
- ✗
Design pipelines with low-code components to reduce development time
Why it's wrong here
Low-code components trade flexibility for speed, and ML pipelines need custom training, evaluation and deployment logic that low-code tools cannot express, so they hinder reproducibility. It is tempting because low-code accelerates delivery, and would be correct for standard application workflows rather than bespoke ML pipeline stages.
- ✗
Write unit tests for every training job
Why it's wrong here
Unit tests on training jobs verify model code in isolation but do not validate data schemas, feature transformations or pipeline components, so they miss the failure modes CI/CD must catch. It is tempting because testing is a genuine CI/CD practice, and would be correct for application code rather than data and pipeline validation.
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