easyMultiple Select
PMLE Practice Question: Which TWO are best practices for building ML…
Which TWO are best practices for building ML pipelines on Vertex AI Pipelines?
⚠ Common exam trap
Google Cloud often tests the distinction between general-purpose orchestration tools (Cloud Composer, Cloud Build) and ML-specific pipeline services (Vertex AI Pipelines), expecting candidates to recognize that container-based components and the Kubeflow Pipelines SDK are the correct building blocks for ML pipelines on Vertex AI.
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 a container-based approach for each component
Option C is correct because Vertex AI Pipelines executes each pipeline step as a containerized component, so packaging each component as a Docker container (with its dependencies and code) ensures reproducibility, portability, and consistent execution across environments. Option D is correct because Vertex AI Pipelines is built on Kubeflow Pipelines, and the Kubeflow Pipelines SDK (the `kfp` package, e.g., `@dsl.pipeline` and `@component` decorators) is the supported, idiomatic way to define, compile, and submit pipelines to Vertex AI. Option A is wrong because storing models without versioning breaks lineage, rollback, and reproducibility; models should be registered in Vertex AI Model Registry with versioning. Option B is wrong because Cloud Build is a CI/CD build service, not a pipeline orchestrator for ML workflows. Option E is wrong because Cloud Composer (managed Apache Airflow) is a general workflow orchestrator, not the primary tool for authoring and running Vertex AI Pipelines.
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 trained models in Cloud Storage without versioning
Why it's wrong here
Unversioned Cloud Storage objects break reproducibility and rollback, since Vertex AI Pipelines relies on model versioning and registry lineage to trace artefacts. Cloud Storage is appropriate for raw artefacts and checkpoints, but trained models belong in the Vertex AI Model Registry with versions.
- ✗
Use Cloud Build as the pipeline orchestrator
Why it's wrong here
Vertex AI Pipelines is itself the orchestrator; Cloud Build suits building container images and CI tasks, not authoring or scheduling ML pipeline DAGs. It would be correct for compiling and pushing custom training images, but it cannot replace Vertex AI Pipelines' managed execution, lineage tracking and artefact handling.
- ✓
Use a container-based approach for each component
Why this is correct
Containerising each component gives every pipeline step a reproducible, dependency-isolated runtime, so Vertex AI Pipelines can execute steps consistently across environments. This satisfies the portability and reproducibility requirements of production ML pipelines, since each component runs as a self-contained image.
- ✓
Define pipelines using the Kubeflow Pipelines SDK
Why this is correct
Defining pipelines with the Kubeflow Pipelines SDK lets Vertex AI Pipelines compile and orchestrate them natively, producing reusable, versioned pipeline definitions. This satisfies the requirement for a supported authoring approach that integrates with Vertex AI's managed execution, caching and artefact tracking.
- ✗
Use Cloud Composer as the primary pipeline tool
Why it's wrong here
Cloud Composer orchestrates general workflows via Airflow, but Vertex AI Pipelines already provides managed ML orchestration with native artefact lineage and Vertex integration. Composer would be the right choice for complex cross-service Airflow DAGs, not as the primary tool for Vertex AI Pipelines.
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