MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO services can be used to orchestrate a machine learning pipeline?
⚠ Common exam trap
Many candidates confuse data preparation or storage services (like AWS Glue or Amazon Redshift) with orchestration services, or they incorrectly assume that a labeling service (Ground Truth) can manage pipeline steps, when in fact orchestration requires a service that can sequence and manage dependencies between distinct ML tasks.
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
✓
Amazon SageMaker Pipelines
Amazon SageMaker Pipelines is a purpose-built service for creating, automating, and managing end-to-end machine learning workflows. It provides direct integration with SageMaker's training, tuning, and deployment steps, allowing you to define a directed acyclic graph (DAG) of ML steps that can be triggered on a schedule or by events. AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services into flexible workflows. It can orchestrate ML pipelines by integrating with SageMaker, Lambda, and other services, making it a viable alternative for complex, multi-step workflows that may span beyond SageMaker's native capabilities. Both services are capable of orchestrating ML pipelines.
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 SageMaker Pipelines
Why this is correct
SageMaker Pipelines is designed for ML pipeline orchestration.
- ✗
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth is for labeling.
- ✓
AWS Step Functions
Why this is correct
Step Functions can orchestrate multi-step workflows.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse.
- ✗
AWS Glue
Why it's wrong here
Glue is for ETL, not general 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
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.