Quick Answer
The answer is AWS Step Functions, which serves as the primary orchestration service for managing end-to-end machine learning pipelines on AWS. Step Functions allows you to coordinate multiple AWS services—such as SageMaker training jobs, model deployments, and Lambda functions—into a state machine workflow, handling dependencies, retries, and parallel execution automatically. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how to build reliable, repeatable ML pipelines without custom scripting; a common trap is confusing orchestration with individual data processing services like Glue (for ETL) or Athena (for querying), which handle specific tasks rather than workflow coordination. Remember that orchestration is about sequencing and state management, not data transformation or warehousing. A helpful memory tip: think of Step Functions as the “conductor” of your ML orchestra, ensuring every instrument (service) plays its part in the right order.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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?
Amazon SageMaker Pipelines
SageMaker Pipelines is designed for ML pipeline orchestration.
Amazon SageMaker Ground Truth
Why wrong: Ground Truth is for labeling.
AWS Step Functions
Step Functions can orchestrate multi-step workflows.
Amazon Redshift
Why wrong: Redshift is a data warehouse.
AWS Glue
Why wrong: Glue is for ETL, not general orchestration.
Answer choices
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
Options A and D are correct. Option B is wrong because it's for labeling. Option C is wrong because it's for data transformation. Option E is wrong because it's for data warehousing.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Machine Learning Implementation and Operations — study guide chapter
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker Pipelines — Options A and D are correct. Option B is wrong because it's for labeling. Option C is wrong because it's for data transformation. Option E is wrong because it's for data warehousing.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 2026
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.
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