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PDE Practice Question: Automate model retraining and deployment whenever…
A company wants to automate model retraining and deployment whenever new training data becomes available. Which service should be used to orchestrate the end-to-end workflow?
⚠ Common exam trap
It's easy for candidates to confuse Cloud Composer (a general-purpose Airflow service) with Vertex AI Pipelines, but the exam expects you to recognize that Vertex AI Pipelines is the ML-specific, fully managed solution for end-to-end ML workflow orchestration, while Cloud Composer requires more manual setup and lacks native Vertex AI integration.
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 choice because it is a managed service specifically designed to orchestrate and automate end-to-end ML workflows, including model retraining and deployment triggered by new data. It allows you to define pipelines as a directed acyclic graph (DAG) of steps using the Kubeflow Pipelines SDK or pre-built components, and it integrates natively with other Vertex AI services for training, evaluation, and deployment.
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 Build
Why it's wrong here
Cloud Build executes build, test and deploy pipelines triggered by source or repository events; it does not schedule data-arrival triggers or coordinate multi-step ML workflows. It is tempting because it handles the deployment half, and would be correct if the task were only containerising and deploying a model after training completed elsewhere.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines orchestrates the full retraining-to-deployment workflow, triggered when new training data arrives. It chains data preprocessing, training, evaluation and deployment as managed pipeline steps, satisfying the stem's requirement to automate the end-to-end process rather than run isolated jobs.
- ✗
Cloud Scheduler
Why it's wrong here
Cloud Scheduler fires jobs on cron or time-based schedules; it cannot react to new training data arriving, nor orchestrate dependent retraining and deployment steps. It is tempting because it triggers recurring work, and would be correct if retraining ran on a fixed timetable rather than on data-availability events.
- ✗
Cloud Composer
Why it's wrong here
Cloud Composer orchestrates pipelines via directed acyclic graphs, but it is not the managed, serverless option for event-driven ML retraining and deployment that Vertex AI Pipelines provides. It is tempting because it genuinely coordinates multi-step workflows, and would be correct for complex custom DAGs spanning non-ML systems.
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This PDE 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 PDE exam.