hardMultiple Choice
PDE Practice Question: A data scientist developed a model using custom…
A data scientist developed a model using custom training on Vertex AI. They want to automate the entire training-to-deployment process. Which service should they use?
⚠ Common exam trap
A common mix-up: candidates confuse general-purpose orchestration (Cloud Composer) with ML-specific pipeline orchestration (Vertex AI Pipelines), overlooking that Vertex AI Pipelines provides built-in ML artifact tracking and native integration with Vertex AI training and prediction services.
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 provides a fully managed, serverless orchestration service specifically designed to automate ML workflows, including custom training, hyperparameter tuning, evaluation, and deployment. It integrates natively with Vertex AI services and supports Kubeflow Pipelines SDK or TFX for defining reproducible, end-to-end pipelines, making it the ideal solution for automating the entire training-to-deployment process.
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 Composer
Why it's wrong here
Cloud Composer orchestrates general workflows via Airflow DAGs, but it does not natively integrate Vertex AI pipeline components, artefacts or model registry steps. It is tempting for cross-system scheduling, yet Vertex AI Pipelines handles the training-to-deployment lifecycle directly.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines orchestrates the full ML workflow as a directed acyclic graph, chaining custom training jobs into evaluation and endpoint deployment steps. This automates the entire training-to-deployment process the stem requires, unlike standalone training or model registry components.
- ✗
Cloud Build
Why it's wrong here
Cloud Build executes CI/CD build and pipeline steps, but it does not orchestrate the multi-stage Vertex AI training-to-deployment workflow with scheduling, triggers and pipeline dependencies. It is tempting as a generic automation tool, yet Vertex AI Pipelines is purpose-built for that orchestration.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions runs short event-driven snippets, not multi-step ML workflows; it cannot orchestrate custom training jobs or endpoint deployment. It is tempting because it triggers pipeline steps on events, which suits lightweight glue tasks, but Vertex AI Pipelines is built for exactly this training-to-deployment orchestration.
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
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