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PMLE Practice Question: An MLOps team is implementing a CI/CD pipeline…
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?
⚠ Common exam trap
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.
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.
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.
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
Eventarc triggers on Cloud Source Repository push events invoke a stateless Cloud Run service, but the stem requires the pipeline to include training, evaluation, and conditional deployment—a multi-step workflow that Cloud Run alone cannot orchestrate. It is tempting because Eventarc is the natural choice for reacting to repository events in a serverless manner, and it would be correct if the goal were simply to trigger a single stateless action, such as running a lightweight validation script, rather than a multi-hour, multi-stage ML pipeline.
- ✗
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.
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