PMLE Automating and Orchestrating ML Pipelines Practice Question
A team wants to implement CI/CD for their ML pipeline using Cloud Build. They want to automatically compile and deploy the pipeline when code is pushed to the main branch. Which three steps should they include in the Cloud Build configuration? (Choose three.)
⚠ Common exam trap
A common trap is confusing build-time actions (compilation, upload, registration) with runtime actions (execution, scheduling). Candidates often mistakenly include immediate pipeline execution as a CI/CD step instead of focusing on deploying and registering the pipeline artifact.
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
✓
Create or update the pipeline in Vertex AI using the compiled file
Option D is correct because the Cloud Build configuration must first set up the environment by installing the Kubeflow Pipelines (KFP) SDK and then compile the pipeline definition into a compiled YAML/JSON artifact, which is the essential build step for a CI/CD ML pipeline. Option B is correct because the compiled pipeline artifact needs to be uploaded to a Cloud Storage bucket so it can be referenced and used by Vertex AI when creating or updating the pipeline. Option A is correct because the final deployment step is to create or update the pipeline in Vertex AI using the compiled file, which is the actual CD action that makes the pipeline available in Vertex AI Pipelines. Option C is not correct because running the pipeline immediately after deployment is not a required CI/CD configuration step; deployment and execution are separate concerns, and the scenario only asks to compile and deploy on push. Option E is not correct because Cloud Scheduler is used for time-based triggering, whereas the scenario requires triggering on code push to the main branch, which is handled by Cloud Build triggers, not Cloud Scheduler.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create or update the pipeline in Vertex AI using the compiled file
Why this is correct
Creating or updating the pipeline in Vertex AI registers the compiled pipeline definition, making it runnable and schedulable. This satisfies the deployment half of the CI/CD requirement, since compilation alone leaves the pipeline unregistered and unable to execute on Vertex AI.
- ✓
Upload the compiled pipeline to Cloud Storage
Why this is correct
Uploading the compiled pipeline to Cloud Storage gives Vertex AI a durable GCS URI to reference when creating or updating the pipeline. This satisfies the stem's deployment requirement, since Vertex AI pipeline jobs read their compiled specification from Cloud Storage rather than local build artefacts.
- ✗
Run the pipeline immediately after deployment
Why it's wrong here
Running the pipeline immediately after deployment executes the ML workflow as a build step, not a CI/CD action; Cloud Build should compile, deploy, and register the pipeline instead. It is tempting because pipeline execution validates the artefact, but that belongs to scheduled or triggered runs after release.
- ✓
Install KFP SDK and compile the pipeline
Why this is correct
Installing the KFP SDK and compiling the pipeline converts the Python pipeline definition into a compiled YAML specification. This satisfies the stem's build stage, producing the artefact that subsequent Cloud Build steps upload to Cloud Storage and register with Vertex AI.
- ✗
Configure Cloud Scheduler to trigger on push
Why it's wrong here
Cloud Scheduler triggers time-based or Pub/Sub jobs, not source-code push events; Cloud Build triggers subscribe to repository changes directly. It is tempting because Scheduler automates invocation, but it would only be correct for cron-style retraining, not main-branch commit-driven builds.
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Same concept, more angles
1 more way 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. A team is implementing CI/CD for ML using Cloud Build. They want to trigger a training pipeline in Vertex AI whenever a new model code is pushed to the main branch of the repository. Which Cloud Build configuration should they use to achieve this?
medium- A.Set up a Cloud Build trigger that runs on push to any branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.
- B.Use a Cloud Scheduler job to periodically check for new commits on main and trigger Cloud Build.
- C.Use Cloud Functions to watch the repository and call Cloud Build on push to main.
- ✓ D.Set up a Cloud Build trigger that runs on push to main branch, and in the build step, use gcloud to submit a Vertex AI Pipeline job.
Why D: Cloud Build triggers can be configured to fire specifically on pushes to the main branch. The build step then uses the gcloud command to submit a Vertex AI Pipeline job, which directly integrates the CI/CD pipeline with Vertex AI's orchestration. This approach is event-driven, immediate, and requires no additional services or polling.
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.