- A
Use Eventarc to listen for Cloud Source Repository push events and invoke a Cloud Run service that starts the pipeline.
Why wrong: This is possible but not the simplest; Cloud Build is more straightforward for CI/CD.
- B
Use an Artifact Registry trigger to detect new model images and then start the pipeline.
Why wrong: Artifact Registry triggers are for container images, not source code changes.
- C
Set up a Cloud Scheduler job that runs every 2 hours and triggers a Vertex AI Pipeline run.
Why wrong: Cloud Scheduler is for scheduled, not event-driven triggers.
- D
Configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories; in the build config, use steps to run the pipeline via the Vertex AI API.
Cloud Build triggers are designed for source code events and can orchestrate ML pipelines.
Quick Answer
The answer is to configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories, using build steps to run the pipeline via the Vertex AI API. This is correct because Cloud Build triggers are designed to fire on source code changes to a specific branch, and within the build configuration you can invoke the Vertex AI Pipeline API using `gcloud` or `curl` steps to orchestrate training, evaluation, and conditional deployment. On the Google Professional Machine Learning Engineer exam, this tests your understanding of how to implement a CI/CD trigger for ML pipeline on branch push, distinguishing Cloud Build’s native branch-based triggers from alternatives like Cloud Functions or Pub/Sub, which are better suited for event-driven triggers on storage changes rather than source control events. A common trap is choosing a Cloud Function trigger on Cloud Source Repositories, but Cloud Build is the native, integrated service for branch-based CI/CD. Memory tip: think “Branch push → Cloud Build → Vertex AI Pipeline” as the direct chain for automated ML pipeline starts on source code changes.
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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 is implementing a CI/CD pipeline for a TensorFlow model on Vertex AI. The model training job takes 2 hours and produces a SavedModel. The team wants to automatically trigger a new pipeline run whenever a change is pushed to the 'main' branch of their source repository. The pipeline should include training, evaluation, and if metrics exceed a threshold, deploy the model to a Vertex AI endpoint. Which trigger configuration should they use?
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
Configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories; in the build config, use steps to run the pipeline via the Vertex AI API.
Option D is correct because Cloud Build triggers can be configured to watch a specific branch (e.g., 'main') in Cloud Source Repositories and automatically execute a build configuration. Within that build config, you can use the `gcloud` or `curl` steps to invoke the Vertex AI Pipeline API, which starts the training, evaluation, and conditional deployment workflow. This directly matches the requirement for a branch-based push trigger that orchestrates the full ML pipeline.
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.
- ✗
Use Eventarc to listen for Cloud Source Repository push events and invoke a Cloud Run service that starts the pipeline.
Why it's wrong here
This is possible but not the simplest; Cloud Build is more straightforward for CI/CD.
- ✗
Use an Artifact Registry trigger to detect new model images and then start the pipeline.
Why it's wrong here
Artifact Registry triggers are for container images, not source code changes.
- ✗
Set up a Cloud Scheduler job that runs every 2 hours and triggers a Vertex AI Pipeline run.
Why it's wrong here
Cloud Scheduler is for scheduled, not event-driven triggers.
- ✓
Configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories; in the build config, use steps to run the pipeline via the Vertex AI API.
Why this is correct
Cloud Build triggers are designed for source code events and can orchestrate ML pipelines.
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 for source code changes) and artifact-based triggers (Artifact Registry for new images), leading candidates to confuse the two when the requirement is to start a pipeline from a code push.
Detailed technical explanation
How to think about this question
Under the hood, Cloud Build triggers use a webhook or polling mechanism to detect pushes to a specified branch in Cloud Source Repositories, GitHub, or Bitbucket. The build config (cloudbuild.yaml) can include a step that calls the Vertex AI Pipeline API via `gcloud ai pipelines run` or a direct REST call, passing parameters like the model training script path and evaluation thresholds. A real-world nuance: if the pipeline takes 2 hours, the Cloud Build trigger should be configured with a longer timeout (default is 1 hour) and use asynchronous pipeline submission to avoid blocking the build step.
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.
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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: Configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories; in the build config, use steps to run the pipeline via the Vertex AI API. — Option D is correct because Cloud Build triggers can be configured to watch a specific branch (e.g., 'main') in Cloud Source Repositories and automatically execute a build configuration. Within that build config, you can use the `gcloud` or `curl` steps to invoke the Vertex AI Pipeline API, which starts the training, evaluation, and conditional deployment workflow. This directly matches the requirement for a branch-based push trigger that orchestrates the full ML pipeline.
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.
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Last reviewed: Jun 30, 2026
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.
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