easyMultiple Choice
PMLE Practice Question: A data scientist has trained a model using Vertex…
A data scientist has trained a model using Vertex AI Training and wants to deploy it to a Vertex AI Endpoint for online predictions. Which orchestration service should be used to automate the deployment step after training completes?
⚠ Common exam trap
Google Cloud often tests the distinction between general-purpose compute services (Cloud Functions, App Engine) and ML-specific orchestration tools (Vertex AI Pipelines), trapping candidates who think any serverless or CI/CD tool can handle the unique requirements of ML workflow automation.
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
Vertex AI Pipelines is the correct orchestration service because it is purpose-built for automating and managing end-to-end ML workflows on Google Cloud. It allows you to define a pipeline that includes both the training step (using Vertex AI Training) and the subsequent deployment step (creating or updating a Vertex AI Endpoint) as a single, repeatable, and monitored workflow. This ensures that after training completes, the model is automatically deployed without manual intervention, leveraging the pipeline's ability to pass artifacts and trigger conditional logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines orchestrates the full ML workflow as a directed acyclic graph, so a deployment component can be triggered automatically once the training component succeeds. This satisfies the stem's requirement to automate deployment to a Vertex AI Endpoint after training completes, without manual intervention.
- ✗
App Engine
Why it's wrong here
App Engine hosts web applications and HTTP services, not pipeline steps; it cannot trigger or sequence a Vertex AI model deployment. It is tempting because it is a managed Google Cloud compute platform, but that role belongs to Vertex AI Pipelines or Cloud Functions acting as orchestration triggers.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions runs event-driven snippets, but it lacks the ML pipeline components to sequence training and deployment steps with artefact tracking. It is tempting because it responds to events, and it would be correct for lightweight glue logic, but Vertex AI Pipelines provides the native training-to-deployment orchestration.
- ✗
Cloud Build
Why it's wrong here
Cloud Build executes CI/CD pipelines triggered by source or repository events, not by a Vertex AI training job completing. It is tempting because Cloud Build automates build and deploy steps, and it would be correct for containerising and deploying code from a Git push, but Vertex AI Pipelines orchestrates ML workflow steps.
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