hardMultiple Choice
PMLE Practice Question: A data scientist uses Vertex AI Pipelines to…
A data scientist uses Vertex AI Pipelines to orchestrate an ML workflow. They want to reuse a component from Google's curated repository. What is the recommended way to incorporate it?
⚠ Common exam trap
Watch out — candidates often confuse the 'aiplatform' SDK (used for direct API calls) with the pipeline components SDK, or assume that copying code is acceptable for reusability, when Google specifically recommends using the curated prebuilt components to ensure compatibility and reduce maintenance overhead.
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 prebuilt components from the Google Cloud Pipeline Components repository
Google provides a curated set of prebuilt components in the Google Cloud Pipeline Components repository, which are designed to be directly imported and used within Vertex AI Pipelines. These components encapsulate common ML tasks (e.g., model training, deployment) and are maintained by Google, ensuring compatibility and reducing custom code. Using them is the recommended approach to avoid reinventing the wheel and to leverage Google's best practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Import the component from Google Cloud Build
Why it's wrong here
Cloud Build compiles containers and runs CI/CD builds; it holds no curated Vertex AI component catalogue to import from. It is tempting because Cloud Build does build custom component images, and would be correct when packaging your own component container before registering it in Artifact Registry.
- ✗
Use the 'aiplatform' Python SDK to define the component
Why it's wrong here
The aiplatform SDK defines and submits custom pipeline components you author yourself; it does not fetch curated repository components. It is tempting because the SDK is the standard programmatic route for building pipelines, and would be correct when defining bespoke components from scratch rather than reusing Google's prebuilt catalogue.
- ✓
Use prebuilt components from the Google Cloud Pipeline Components repository
Why this is correct
Google Cloud Pipeline Components are published, versioned Vertex AI pipeline components that can be loaded and inserted directly into a pipeline definition, giving tested implementations without writing component code. Referencing them from the curated repository is the documented reuse path, satisfying the reuse requirement.
- ✗
Copy the component code into the pipeline definition
Why it's wrong here
Copying the code duplicates the component, so upstream fixes and version updates in the curated repository are never inherited. It is tempting because pasting code gives immediate local control and works when a component must be heavily modified, but it abandons the reuse the question asks for.
Go deeper
Related to this question
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.