PMLE Automating and Orchestrating ML Pipelines Practice Question
You are building a CI/CD pipeline for an ML model using Cloud Build. When code is pushed to the main branch, you want to automatically build a training image, run a Vertex AI pipeline, and if the model evaluation passes, deploy it to a staging endpoint. Which two components are essential for this CI/CD pipeline?
⚠ Common exam trap
Google often tests the distinction between event-driven triggers (Cloud Build trigger on push) and schedule-based triggers (Cloud Scheduler), so candidates mistakenly pick Cloud Scheduler when the requirement is for a code-push event.
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
✓
Vertex AI Pipelines to orchestrate training and evaluation.
Option D is correct because a Cloud Build trigger is the native mechanism that responds to push events on the main branch, automatically starting the build of the training image and initiating the pipeline workflow. Option C is correct because Vertex AI Pipelines orchestrates the training and evaluation steps, and its evaluation component determines whether the model passes the quality gate before deployment to the staging endpoint. Option A is incorrect because Cloud Scheduler triggers on a time-based schedule, not on code push events, so it does not satisfy the push-to-main requirement. Option B is incorrect because Cloud Functions is not the deployment mechanism for Vertex AI models; deployment is handled through Vertex AI endpoints or pipeline components. Option E is incorrect because Vertex AI Continuous Training is a managed retraining feature, not the CI/CD orchestration component needed to trigger and gate deployments on code changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Scheduler to trigger the pipeline on a schedule.
Why it's wrong here
Cloud Scheduler issues time-based or cron triggers, so it cannot react to a push to the main branch. The pipeline needs a Cloud Build trigger bound to the repository event. Cloud Scheduler is correct for periodic batch jobs, such as nightly retraining or scheduled data exports.
- ✗
Cloud Functions to deploy the model.
Why it's wrong here
Cloud Functions runs short event-driven code and cannot host the build steps, pipeline execution or evaluation-gated deployment logic. Cloud Build performs the image build and orchestrates the pipeline, with Vertex AI handling training and deployment. Cloud Functions suits lightweight glue tasks, not CI/CD orchestration.
- ✓
Vertex AI Pipelines to orchestrate training and evaluation.
Why this is correct
Vertex AI Pipelines orchestrates the training and evaluation steps as a managed DAG, executing each component container in sequence and surfacing the evaluation metrics that gate deployment. This satisfies the stem's requirement to run a pipeline and conditionally promote the model only when evaluation passes.
- ✓
Cloud Build trigger configured to respond to push events to the main branch.
Why this is correct
A Cloud Build trigger responding to push events on the main branch satisfies the automation constraint, starting the build-and-deploy workflow whenever code is pushed. It is the entry point that initiates image building and pipeline execution.
- ✗
Vertex AI Continuous Training service.
Why it's wrong here
Vertex AI Continuous Training is a managed scheduling feature that retrains models on a cadence when new data arrives; it does not build images or run pipelines on a code push. It fits recurring retraining triggered by data drift, not commit-triggered CI/CD orchestration.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.