PMLE Automating and Orchestrating ML Pipelines Practice Question
A team wants to implement continuous training for their ML model. The pipeline should be triggered when new training data arrives in a Cloud Storage bucket. Which combination of services should they use?
⚠ Common exam trap
PMLE often tests the difference between event-driven triggers (Cloud Functions) and time-based triggers (Cloud Scheduler), catching candidates who pick Scheduler for data-arrival events.
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
✓
Cloud Storage → Cloud Functions → Vertex AI Pipelines
The correct combination is Cloud Storage → Cloud Functions → Vertex AI Pipelines. Cloud Storage hosts the new training data; a Cloud Function triggered by an object-finalize event (or Pub/Sub notification) detects the new data and invokes a Vertex AI Pipeline to retrain the model. This creates a fully event-driven continuous training pipeline without polling or manual intervention.
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 Storage → BigQuery → Vertex AI Pipelines
Why it's wrong here
BigQuery is an analytics warehouse, not an event source; it cannot detect a new Cloud Storage object and trigger the pipeline. It would be correct when training data already resides in BigQuery tables and the pipeline reads features from SQL, but here the arrival event itself must drive the trigger.
- ✓
Cloud Storage → Cloud Functions → Vertex AI Pipelines
Why this is correct
A Cloud Storage object-change event triggers a Cloud Functions function, which calls the Vertex AI API to launch the training pipeline. This event-driven chain satisfies the stem's requirement to retrain automatically when new training data lands in the bucket.
- ✗
Cloud Storage → Cloud Build → Vertex AI Pipelines
Why it's wrong here
Cloud Build executes builds and CI tasks; it has no native Cloud Storage object-finalise trigger that launches Vertex AI Pipelines. It would be correct for building or testing container images before deployment, but the stem requires an event-driven trigger reacting directly to new training data.
- ✗
Cloud Storage → Cloud Scheduler → Vertex AI Pipelines
Why it's wrong here
Cloud Scheduler issues time-based cron jobs, so it cannot react to an object arriving in Cloud Storage. It would be correct for recurring retraining on a fixed schedule, but the stem demands event-driven triggering the moment new training data lands in the bucket.
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 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.