- A
Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob
Cloud Build uses build config to define steps, including submitting pipeline jobs.
- B
Use Cloud Functions to retrain the model each time a build completes
Why wrong: Cloud Build steps directly call Vertex AI; no need for Cloud Functions.
- C
Set up a Cloud Scheduler job to poll for new build artifacts
Why wrong: Cloud Build triggers are event-driven, not polling-based.
- D
Define the training and deployment steps in a Vertex AI Pipeline and submit it from Cloud Build
The pipeline job includes all steps; Cloud Build orchestrates the submission.
- E
Configure a Cloud Build trigger to run on commits to the source repository
This enables continuous integration for model code and pipeline definitions.
Quick Answer
The answer is to configure a Cloud Build trigger to run on commits to the source repository. This is correct because the cloudbuild.yaml file can define a step that builds a custom training container and submits it as a Vertex AI PipelineJob, creating a fully automated ML CI/CD pipeline where every code change triggers model retraining and deployment. On the Google Professional Machine Learning Engineer exam, this pattern tests your understanding of how Cloud Build acts as the CI orchestrator that invokes Vertex AI Pipelines, a common scenario in the "ML solution testing, automating, and deploying" section. A frequent trap is thinking you need a separate CI tool like Jenkins or that Vertex AI alone handles the commit trigger, but Cloud Build provides the native Git integration. Memory tip: think "Commit triggers Container, Container triggers Pipeline" — Cloud Build is the gatekeeper that connects your source code to Vertex AI’s orchestration.
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and 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
Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob
Option A is correct because Cloud Build's cloudbuild.yaml can define a step that builds a custom training container and submits it as a Vertex AI PipelineJob. This directly automates the ML pipeline by using Cloud Build to trigger a Vertex AI pipeline, which is the recommended pattern for CI/CD of ML workflows.
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.
- ✓
Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob
Why this is correct
Cloud Build uses build config to define steps, including submitting pipeline jobs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Functions to retrain the model each time a build completes
Why it's wrong here
Cloud Build steps directly call Vertex AI; no need for Cloud Functions.
- ✗
Set up a Cloud Scheduler job to poll for new build artifacts
Why it's wrong here
Cloud Build triggers are event-driven, not polling-based.
- ✓
Define the training and deployment steps in a Vertex AI Pipeline and submit it from Cloud Build
Why this is correct
The pipeline job includes all steps; Cloud Build orchestrates the submission.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Configure a Cloud Build trigger to run on commits to the source repository
Why this is correct
This enables continuous integration for model code and pipeline definitions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between event-driven triggers (Cloud Build triggers, Pub/Sub) and polling mechanisms (Cloud Scheduler, Cloud Functions) — the trap here is that candidates may think polling or separate functions are needed for automation, when in fact Cloud Build's native triggers and pipeline submission are the correct, integrated approach.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Build uses a cloudbuild.yaml with steps that can invoke the Vertex AI Pipeline API via the 'gcloud' command or a custom container. The pipeline definition (typically a JSON or YAML file) is compiled using the Vertex AI Pipelines SDK, and Cloud Build can submit it as a PipelineJob using the 'aiplatform' service. This pattern ensures that each code commit triggers a reproducible training and deployment workflow, with artifacts stored in Cloud Storage and metadata tracked in Vertex ML Metadata.
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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Automating and orchestrating ML pipelines — study guide chapter
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Automating and orchestrating ML pipelines practice questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and orchestrating ML pipelines — This question tests Automating and orchestrating ML pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob — Option A is correct because Cloud Build's cloudbuild.yaml can define a step that builds a custom training container and submits it as a Vertex AI PipelineJob. This directly automates the ML pipeline by using Cloud Build to trigger a Vertex AI pipeline, which is the recommended pattern for CI/CD of ML workflows.
What should I do if I get this PMLE 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. The exhibit shows a Cloud Build configuration. An ML engineer wants to automate the deployment of a model to Vertex AI after training. What is missing in this config to successfully deploy the model?
medium- A.A step to upload the training image to Artifact Registry
- ✓ B.A step to build the serving container image
- C.A step to run unit tests
- D.A step to create the Vertex AI Endpoint
Why B: The Cloud Build configuration shown is for training a model, but to deploy it to Vertex AI, a serving container image must be built and pushed to Artifact Registry. Vertex AI requires a custom serving container (or a prebuilt one) to host the model for predictions. Without a step to build the serving container image (e.g., using a Dockerfile that includes the model and serving dependencies), the deployment will fail because there is no runnable image to deploy to the endpoint.
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Last reviewed: Jun 30, 2026
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