Quick Answer
The answer is AWS Step Functions, the serverless orchestration service that coordinates multi-step workflows across AWS services. This is correct because Step Functions excels at chaining complex, event-driven sequences like triggering a Lambda function for approval checks, calling the SageMaker CreateEndpoint API to deploy a model from the registry to a staging endpoint, and implementing rollback logic on failure—all without managing servers. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish orchestration from automation: SageMaker Pipelines handles training and registration, but Step Functions is the glue for post-training deployment workflows. A common trap is choosing SageMaker Pipelines for everything, but remember Pipelines stops at model registration; Step Functions takes over for deployment orchestration. Memory tip: think “Pipelines builds, Step Functions deploys”—or simply recall the mnemonic “Step in for deployment.”
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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.
A company uses SageMaker Pipelines to train and register models. They want to automate the deployment of approved models from the model registry to a staging endpoint. Which service should they use to orchestrate the deployment workflow?
AWS Step Functions
Step Functions can orchestrate SageMaker API calls and integrate with Model Registry.
AWS CloudFormation
Why wrong: CloudFormation is for infrastructure as code.
Amazon EventBridge
Why wrong: EventBridge triggers simple events, not complex workflows.
AWS CodePipeline
Why wrong: CodePipeline is for CI/CD of source code, not specific to ML model deployment workflows.
Answer choices
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 is a serverless orchestration service designed to coordinate multiple AWS services into flexible, event-driven workflows. For SageMaker Pipelines, Step Functions can trigger model deployment from the registry to a staging endpoint by chaining actions like invoking a Lambda function for approval checks, calling SageMaker's CreateEndpoint API, and handling rollback logic on failure.
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.
AWS Step Functions
Why this is correct
Step Functions can orchestrate SageMaker API calls and integrate with Model Registry.
Related concept
Read the scenario before looking for a memorised answer.
AWS CloudFormation
Why it's wrong here
CloudFormation is for infrastructure as code.
Amazon EventBridge
Why it's wrong here
EventBridge triggers simple events, not complex workflows.
AWS CodePipeline
Why it's wrong here
CodePipeline is for CI/CD of source code, not specific to ML model deployment workflows.
Common exam traps
AWS often tests the distinction between orchestration (Step Functions) and event routing (EventBridge) or CI/CD (CodePipeline), leading candidates to pick EventBridge because they confuse event-driven triggers with the need for sequential workflow coordination.
Detailed technical explanation
Under the hood, Step Functions uses Amazon States Language (ASL) to define state machines that can execute tasks like Lambda functions, SageMaker API calls, and even direct integrations with SageMaker Pipelines via the AWS SDK. A real-world scenario involves using a Step Functions state machine that waits for a model approval event from the SageMaker model registry (via EventBridge), then deploys the model to a staging endpoint with automatic rollback if health checks fail, all while logging each step to CloudWatch.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Step Functions — AWS Step Functions is the correct choice because it is a serverless orchestration service designed to coordinate multiple AWS services into flexible, event-driven workflows. For SageMaker Pipelines, Step Functions can trigger model deployment from the registry to a staging endpoint by chaining actions like invoking a Lambda function for approval checks, calling SageMaker's CreateEndpoint API, and handling rollback logic on failure.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This MLA-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 MLA-C01 exam.
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