mediumMultiple Choice
PDE Practice Question: An MLOps team wants to implement continuous…
An MLOps team wants to implement continuous deployment of ML models using Cloud Build and Vertex AI. They have a GitHub repository with training code. What should they use?
⚠ Common exam trap
The trap here is that candidates may overthink the solution and choose Vertex AI Pipelines (Option B) because it is a dedicated ML orchestration tool, but the question specifically asks for integration with Cloud Build, and a simple Cloud Build trigger with custom steps is the most direct and efficient approach for continuous deployment.
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
✓
Cloud Build trigger with a custom step to run Vertex AI Training job and deploy
It directly addresses the requirement for continuous deployment of ML models using Cloud Build and Vertex AI. A Cloud Build trigger can be configured to fire on GitHub commits, and a custom step in the Cloud Build pipeline can invoke a Vertex AI Training job, followed by deploying the trained model to Vertex AI Endpoints. This provides a fully automated CI/CD pipeline for ML models without additional orchestration overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy using Cloud Run
Why it's wrong here
Cloud Run hosts containerised HTTP services, not ML model endpoints; it cannot register models in Vertex AI or serve predictions through Vertex's prediction API. It suits deploying a lightweight web front end that calls a deployed model, not continuous model deployment from a training repository.
- ✗
Vertex AI Pipelines integrated with Cloud Build
Why it's wrong here
Vertex AI Pipelines orchestrates training and evaluation workflows, not deployment; it lacks the trigger and release mechanics Cloud Build provides. It is the right choice for automating retraining and model evaluation stages, but the stem asks for the deployment step that publishes a trained model to an endpoint.
- ✗
Cloud Functions to monitor GitHub
Why it's wrong here
Cloud Functions cannot trigger Vertex AI model deployment pipelines from repository events; Cloud Build triggers on GitHub commits and runs the build and deploy steps. It is tempting because Cloud Functions is serverless event handling, which would fit webhook processing, but not orchestrating model training and deployment.
- ✓
Cloud Build trigger with a custom step to run Vertex AI Training job and deploy
Why this is correct
A Cloud Build trigger on the GitHub repository fires on commits, and a custom build step invokes a Vertex AI Training job then deploys the resulting model. This wires source control to training and deployment, satisfying the continuous deployment requirement.
Go deeper
Related to this question
About these practice questions
This PDE question is part of Courseiva's 747-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
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.