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Operationalizing machine learning models →mediumMultiple ChoiceObjective-mapped

Continuous Deployment of ML Models Using Cloud Build and Vertex AI

This PDE practice question tests your understanding of operationalizing machine learning models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

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?

Quick Answer

The correct choice is a Cloud Build trigger with a custom step to run a Vertex AI Training job and deploy the model. This works because Cloud Build is Google Cloud’s native CI/CD system, and its triggers can execute custom steps—such as invoking the Vertex AI Training API to retrain a model from a GitHub repository, then deploying the updated artifact to Vertex AI Endpoints for serving. On the Google Professional Data Engineer exam, this scenario tests your understanding of how to integrate CI/CD pipelines with ML workflows, specifically that Cloud Build handles the continuous deployment orchestration while Vertex AI provides the training and serving infrastructure. A common trap is confusing Vertex AI Pipelines (an orchestration tool for ML workflows) with a CI/CD system, or selecting Cloud Functions or Cloud Run, which are event-driven and serverless but lack the pipeline control needed for model training and deployment. Memory tip: think of Cloud Build as the “builder” that triggers training and deployment, not the platform that runs the ML job itself.

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

Option D is correct because 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.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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 is for serving containers, not for ML training and deployment orchestration.

  • ✗

    Vertex AI Pipelines integrated with Cloud Build

    Why it's wrong here

    While Pipelines can be triggered by Cloud Build, the primary CI/CD tool is Cloud Build itself.

  • ✗

    Cloud Functions to monitor GitHub

    Why it's wrong here

    Cloud Functions are not designed for CI/CD pipelines.

  • ✓

    Cloud Build trigger with a custom step to run Vertex AI Training job and deploy

    Why this is correct

    Cloud Build can be configured to trigger on GitHub pushes and run training/deployment steps.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

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.

Detailed technical explanation

How to think about this question

Under the hood, Cloud Build triggers can be configured to watch a GitHub branch or tag, and the custom step can use the `gcloud` command or a Docker container with the Vertex AI SDK to submit a training job. The trained model artifact is then stored in Cloud Storage, and a subsequent step can create or update a Vertex AI Endpoint with the new model. This pattern leverages Cloud Build's native artifact storage and IAM integration, ensuring that only authorized commits trigger deployments.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this PDE question test?

Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Cloud Build trigger with a custom step to run Vertex AI Training job and deploy — Option D is correct because 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.

What should I do if I get this PDE question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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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.