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PDE Practice Question: A company has a trained model stored in Vertex AI…
A company has a trained model stored in Vertex AI Model Registry. They want to automate retraining when new training data arrives in Cloud Storage. Which approach is most efficient?
⚠ Common exam trap
Google Cloud often tests the distinction between event-driven (Cloud Functions) and scheduled (Cloud Scheduler, Cloud Composer) approaches, and candidates mistakenly choose a scheduled option thinking it is simpler, missing the requirement for immediate reaction to new data.
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
✓
Use Cloud Functions triggered by Cloud Storage events to start a Vertex AI Training job
Cloud Functions can be directly triggered by Cloud Storage events (e.g., object finalize) to invoke the Vertex AI Training service via the AI Platform API. This creates an event-driven, serverless pipeline that retrains the model immediately when new data arrives, without polling or manual intervention, making it the most efficient and cost-effective approach.
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 Functions triggered by Cloud Storage events to start a Vertex AI Training job
Why this is correct
A Cloud Function triggered by Cloud Storage object-finalise events reacts immediately to new training data, programmatically launching a Vertex AI Training job. This event-driven pattern satisfies the automation constraint without polling, and is more efficient than scheduled retraining or manual pipeline runs.
- ✗
Use Dataflow to continuously update the model
Why it's wrong here
Dataflow performs streaming and batch data processing; it does not train models or write new versions to the Model Registry. It is tempting because it handles incoming Cloud Storage data, and would be correct for transforming, enriching or moving that data before it reaches a training pipeline.
- ✗
Use Cloud Scheduler to trigger a Cloud Build retraining step
Why it's wrong here
Cloud Scheduler triggers jobs on a fixed timetable, so retraining occurs regardless of whether new training data has actually arrived. It is tempting because it automates recurring pipelines, and would be correct for scheduled batch retraining where freshness of the training data is not a requirement.
- ✗
Schedule a weekly Cloud Composer DAG to check for new data and retrain
Why it's wrong here
Event-driven retraining triggered by a Cloud Storage object-finalise notification retrains on arrival; a weekly DAG polls and delays retraining by up to seven days. Composer suits multi-step orchestration across services with dependencies, not latency-sensitive single-event triggers.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.