easyMultiple Choice
PDE Practice Question: A team wants to retrain a model weekly using new…
A team wants to retrain a model weekly using new data stored in BigQuery. They want to minimize manual effort. Which approach should they use?
⚠ Common exam trap
Google Cloud often tests the distinction between simple scheduling (Cloud Scheduler + Cloud Function) and full ML orchestration (Vertex AI Pipelines), where candidates mistakenly choose the simpler option without considering the need for a managed, scalable ML workflow.
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
✓
Create a Vertex AI Pipeline scheduled via Cloud Scheduler
Vertex AI Pipelines allow you to define a repeatable, automated ML workflow that can be triggered on a schedule via Cloud Scheduler. This minimizes manual effort by handling data extraction from BigQuery, model retraining, and deployment without human intervention, while also providing versioning and monitoring capabilities.
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 Cloud Scheduler to trigger a Cloud Function that retrains
Why it's wrong here
Cloud Scheduler plus a Cloud Function gives only time-based triggering; it lacks pipeline dependency management, retry logic and BigQuery-aware orchestration for weekly retraining. It tempts because it is lightweight and serverless, and suits simple scheduled jobs, but Vertex AI Pipelines or Cloud Composer is needed for managed ML workflow orchestration.
- ✗
Retrain manually in a notebook each week
Why it's wrong here
Manual weekly retraining in a notebook leaves scheduling, data extraction and execution to a human, so it cannot minimise manual effort. It is tempting because notebooks suit exploratory model development and one-off experiments, where an analyst controls each run; automation via scheduled pipelines or BigQuery ML is required for recurring retraining.
- ✗
Use Cloud Composer to orchestrate retraining
Why it's wrong here
Cloud Composer orchestrates general workflows but requires authoring and maintaining DAGs, adding operational effort rather than minimising it. It tempts because it handles complex scheduling and dependencies well, and suits multi-step data pipelines, yet Vertex AI Pipelines provides managed, purpose-built retraining orchestration with less manual overhead.
- ✓
Create a Vertex AI Pipeline scheduled via Cloud Scheduler
Why this is correct
Scheduling a Vertex AI Pipeline with Cloud Scheduler automates the weekly retrain, satisfying the minimal-manual-effort constraint. The pipeline reads the fresh BigQuery data, retrains, and redeploys without intervention, whereas manual notebook runs or ad-hoc jobs would require recurring human triggers.
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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.