MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist wants to use AWS Step Functions to orchestrate a machine learning workflow including data preprocessing, training, and evaluation. Which SageMaker integration is best suited for this purpose?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Pipelines (a dedicated ML pipeline service) with Step Functions, but the question explicitly asks for a SageMaker integration with Step Functions, not a replacement for it.
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 the SageMaker SDK with Step Functions service integrations
AWS Step Functions has a native service integration with SageMaker, allowing you to directly call SageMaker API actions (e.g., CreateTrainingJob, CreateModel, CreateEndpointConfig, CreateEndpoint) without needing intermediate Lambda functions. This integration supports both synchronous and asynchronous invocations, making it ideal for orchestrating a multi-step ML workflow with built-in error handling, retries, and state management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement each step as an AWS Lambda function and call Step Functions
Why it's wrong here
Lambda adds latency and complexity; direct integration is better.
- ✓
Use the SageMaker SDK with Step Functions service integrations
Why this is correct
Step Functions has built-in integrations for SageMaker training, processing, and endpoints.
- ✗
Use SageMaker Pipelines to define the workflow
Why it's wrong here
SageMaker Pipelines is an alternative, but Step Functions integration is more flexible.
- ✗
Use AWS Batch to run the steps sequentially
Why it's wrong here
Batch is for batch computing, not 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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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.