MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a model and wants to automatically retrain the model every week using new data. Which AWS service should be used to orchestrate the retraining pipeline?
⚠ Common exam trap
Test-takers frequently confuse a scheduling service (CloudWatch Events) with a workflow orchestrator (Step Functions), or assume that a single Lambda function can handle the entire pipeline, overlooking the need for state management, error handling, and multi-step coordination.
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
✓
AWS Step Functions
AWS Step Functions is the correct choice because it provides a serverless workflow orchestration service that can coordinate multiple AWS services (e.g., SageMaker training jobs, Lambda functions, and data processing) into a state machine. It supports scheduling via Amazon EventBridge (formerly CloudWatch Events) to trigger the pipeline weekly, and it can handle retries, error handling, and parallel execution, making it ideal for automating a retraining pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon CloudWatch Events
Why it's wrong here
CloudWatch Events can trigger a Lambda on schedule but cannot orchestrate a pipeline.
- ✗
AWS Lambda
Why it's wrong here
Lambda functions are stateless and limited to 15 minutes, not suitable for long-running pipelines.
- ✓
AWS Step Functions
Why this is correct
Step Functions can orchestrate multiple SageMaker API calls and handle retries.
- ✗
AWS Data Pipeline
Why it's wrong here
Data Pipeline is legacy and less integrated with SageMaker than Step Functions.
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
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.