hardMultiple Choice
PMLE Practice Question: A data engineering team wants to orchestrate an…
A data engineering team wants to orchestrate an ML pipeline that includes data preprocessing in Dataflow, AutoML training, and model deployment. They want to minimize operational overhead. Which approach is best?
⚠ Common exam trap
It's easy for candidates to confuse 'orchestration' with 'scheduling' and pick Cloud Scheduler, failing to recognize that a multi-step ML pipeline requires workflow orchestration with dependencies and error handling, not just a time-based 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 Vertex AI Pipelines with custom components
Vertex AI Pipelines with custom components is the best choice because it provides a fully managed, serverless orchestration service that natively integrates with Dataflow, AutoML, and model deployment. This minimizes operational overhead by eliminating the need to manage infrastructure, handle retries, or maintain a separate orchestration server, while offering built-in artifact tracking and pipeline caching.
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 Composer with Apache Airflow DAG
Why it's wrong here
Cloud Composer runs a managed Airflow environment whose cluster and DAG infrastructure you still configure and maintain, adding operational overhead. It is tempting because Airflow excels at orchestrating multi-step pipelines across heterogeneous services, and would be correct if the team already ran Composer or needed custom scheduling logic.
- ✗
Use AI Platform Training with script
Why it's wrong here
AI Platform Training executes a single training job from a script; it cannot sequence Dataflow preprocessing, AutoML training and deployment as one pipeline. It is tempting because it is a managed training service, and would be correct if the task were only running a custom training script on prepared data.
- ✗
Use Cloud Scheduler to trigger Cloud Functions
Why it's wrong here
Cloud Scheduler triggering Cloud Functions provides only time-based invocation, with no dependency tracking, retries or state across Dataflow, AutoML and deployment steps. It is tempting because it is serverless and low-cost, and would be correct for simple periodic jobs such as nightly cleanups.
- ✓
Use Vertex AI Pipelines with custom components
Why this is correct
Vertex AI Pipelines orchestrates Dataflow preprocessing, AutoML training, and deployment as managed, serverless steps, minimising infrastructure to maintain. Custom components wrap the Dataflow job and AutoML task, so the whole workflow runs without the team provisioning or operating separate orchestration infrastructure.
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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