mediumMultiple Choice
PMLE Practice Question: A data scientist uses Vertex AI Workbench to…
A data scientist uses Vertex AI Workbench to train a model and then deploys it to an endpoint. They want to automate the retraining and redeployment pipeline when new data arrives. Which service should they use?
⚠ Common exam trap
PMLE often tests whether candidates pick a general orchestrator (Cloud Composer) when the ML-native, lower-overhead answer (Vertex AI Pipelines) is intended, or confuse a trigger (Cloud Scheduler) with an orchestrator.
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 managed orchestration service for ML workflows on Google Cloud, designed to automate training, evaluation, and deployment steps as a reproducible DAG. It integrates natively with Vertex AI endpoints and supports triggers from new data events, making it the correct choice for automating retraining and redeployment. Cloud Composer can orchestrate, but Vertex AI Pipelines is purpose-built for ML and requires less overhead for this use case.
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 Composer
Why it's wrong here
Cloud Composer orchestrates general DAG workflows but does not itself trigger on new data or manage Vertex AI model and endpoint lifecycle. It is tempting because it is the standard pipeline tool, and would be right for complex multi-step ETL with dependencies rather than event-driven retraining.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines orchestrates the retraining and redeployment workflow as a repeatable DAG, triggered when new data arrives. It satisfies the automation constraint by chaining data ingestion, training, evaluation and endpoint deployment steps without manual intervention.
- ✗
Cloud Scheduler
Why it's wrong here
Cloud Scheduler only fires jobs on a cron timetable; it cannot react to a data-arrival event or invoke Vertex AI training and deployment steps. It is tempting as a simple trigger, and would be correct for a fixed nightly retraining cadence where new data timing is predictable.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions runs short event-driven code but lacks the orchestration to sequence training, evaluation and endpoint deployment as one managed pipeline. It is tempting because it responds to events cheaply, and would be right for lightweight glue logic rather than full Vertex AI pipeline automation.
Go deeper
Related to this question
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.