- A
Amazon SageMaker Pipelines
SageMaker Pipelines is designed for ML pipeline orchestration.
- B
Amazon SageMaker Ground Truth
Why wrong: Ground Truth is for labeling.
- C
AWS Step Functions
Step Functions can orchestrate multi-step workflows.
- D
Amazon Redshift
Why wrong: Redshift is a data warehouse.
- E
AWS Glue
Why wrong: Glue is for ETL, not general orchestration.
Question 1,532 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO services can be used to orchestrate a machine learning pipeline?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, both SageMaker Pipelines and AWS Step Functions use a state machine model to define dependencies and parallel execution of steps. SageMaker Pipelines is tightly coupled with SageMaker resources (e.g., TrainingJob, ProcessingJob, Model) and automatically handles artifact tracking and lineage via the SageMaker SDK. AWS Step Functions, on the other hand, is a general-purpose workflow service that can integrate with any AWS service via API calls, making it suitable for multi-service ML pipelines that span beyond SageMaker, such as invoking Lambda functions for custom preprocessing or triggering Glue jobs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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