hardMultiple ChoiceObjective-mapped
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 is a general workflow orchestration tool, not ML-specific.
- ✓
Vertex AI Pipelines
Why this is correct
Vertex AI Pipelines is purpose-built for ML pipeline orchestration.
- ✗
Cloud Build
Why it's wrong here
Cloud Build is for CI/CD of applications, not ML workflows.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions is event-driven and not suitable for complex pipeline 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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