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PMLE Practice Question: An ML team is designing an automated pipeline to…
An ML team is designing an automated pipeline to retrain a recommendation model every day using new user interaction data stored in BigQuery. The pipeline must be cost-efficient, scalable, and require minimal manual intervention. Which two approaches should they consider?
⚠ Common exam trap
Google Cloud often tests the distinction between batch scheduling (Cloud Scheduler) and continuous streaming (Dataflow), and candidates mistakenly choose Dataflow because they think 'new data' implies real-time, but the requirement is a daily retrain, not a streaming trigger.
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 Scheduler to publish a Pub/Sub message daily, which triggers a Cloud Function that starts the Vertex AI Pipeline.
Cloud Scheduler triggers a Pub/Sub message that invokes a Cloud Function, which starts a Vertex AI Pipeline. This serverless approach is cost-efficient (no idle compute), scales automatically, and requires minimal manual intervention. Option E is correct because Vertex AI Pipelines natively orchestrates ML workflows, and using preemptible VMs reduces training costs by up to 80% while maintaining scalability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy a custom Kubernetes cron job on GKE to run the training script directly.
Why it's wrong here
This adds cluster management overhead.
- ✗
Use Cloud Composer (Airflow) to schedule the pipeline with a DAG.
Why it's wrong here
Overkill for a simple daily schedule; adds complexity.
- ✓
Use Cloud Scheduler to publish a Pub/Sub message daily, which triggers a Cloud Function that starts the Vertex AI Pipeline.
Why this is correct
This provides automated daily triggering with minimal overhead.
- ✗
Use Dataflow to continuously read from BigQuery and trigger training when new data arrives.
Why it's wrong here
Dataflow is for streaming, but the requirement is daily batch.
- ✓
Use Vertex AI Pipelines to define the workflow and preemptible VMs for training to reduce cost.
Why this is correct
Preemptible VMs are cost-effective and Vertex AI Pipelines orchestrates the workflow.
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 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.