- A
An AWS CodePipeline pipeline with approval stage
Why wrong: CodePipeline is a separate service; Step Functions can include approval without CodePipeline.
- B
A task in the state machine that pauses and waits for manual approval via SNS or Lambda
Step Functions can use 'Wait for Task Token' to implement human approval.
- C
Model Registry to store the approved model version after evaluation
Model Registry tracks model versions and can be updated by the state machine.
- D
An Amazon SNS topic for notification of approval status
Why wrong: SNS is a notification service; while it may be used in the approval task, it is not a required component of the state machine itself.
- E
An API call to SageMaker to create or update the production endpoint
Step Functions can call SageMaker APIs directly.
Quick Answer
The answer is the callback pattern with `.waitForTaskToken`, an SNS topic or Lambda function for signaling, and an API call to SageMaker to update the production endpoint. This combination is necessary because Step Functions uses the `.waitForTaskToken` integration to pause the state machine at the approval gate, holding the task token until an external process—triggered by an SNS notification or a Lambda function—sends a `SendTaskSuccess` or `SendTaskFailure` signal back to Step Functions, allowing human review before the final deployment step. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of orchestrating ML workflows with human-in-the-loop controls, a common requirement for production-grade MLOps pipelines. A frequent trap is forgetting that the API call to SageMaker must occur after approval, not before, or confusing the callback pattern with a simple wait state. Memory tip: think "Token, Topic, Task"—the token pauses, the topic notifies, and the task updates the endpoint.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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 is using an AWS Step Functions state machine to orchestrate a multi-step ML deployment. The workflow includes: training a model, evaluating it, registering the model, and deploying to a staging endpoint. They need to implement an approval gate before deploying to production. Which THREE components are necessary to achieve this? (Choose three.)
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
A task in the state machine that pauses and waits for manual approval via SNS or Lambda
Option B is correct because Step Functions can use a task with a callback pattern (`.waitForTaskToken`) to pause the workflow and wait for external manual approval. When combined with an SNS topic or Lambda function that sends a task success or failure signal back to Step Functions, this creates a reliable approval gate. This pattern allows the state machine to halt execution until a human approves or rejects the deployment, which is essential for production deployment control.
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.
- ✗
An AWS CodePipeline pipeline with approval stage
Why it's wrong here
CodePipeline is a separate service; Step Functions can include approval without CodePipeline.
- ✓
A task in the state machine that pauses and waits for manual approval via SNS or Lambda
Why this is correct
Step Functions can use 'Wait for Task Token' to implement human approval.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Model Registry to store the approved model version after evaluation
Why this is correct
Model Registry tracks model versions and can be updated by the state machine.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
An Amazon SNS topic for notification of approval status
Why it's wrong here
SNS is a notification service; while it may be used in the approval task, it is not a required component of the state machine itself.
- ✓
An API call to SageMaker to create or update the production endpoint
Why this is correct
Step Functions can call SageMaker APIs directly.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between a notification-only service (like SNS) and a service that can actively pause and resume a workflow (like Step Functions with task tokens), leading candidates to mistakenly select SNS as a sufficient approval gate component.
Detailed technical explanation
How to think about this question
The Step Functions `.waitForTaskToken` integration pattern works by including a unique task token in the task input, which the external approval process must return via the `SendTaskSuccess` or `SendTaskFailure` API call. This token ensures that only the correct approval signal resumes the correct execution, preventing race conditions. In real-world ML workflows, this pattern is often combined with a SageMaker Model Registry to track approved model versions, ensuring that only validated models proceed to production endpoints.
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.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A task in the state machine that pauses and waits for manual approval via SNS or Lambda — Option B is correct because Step Functions can use a task with a callback pattern (`.waitForTaskToken`) to pause the workflow and wait for external manual approval. When combined with an SNS topic or Lambda function that sends a task success or failure signal back to Step Functions, this creates a reliable approval gate. This pattern allows the state machine to halt execution until a human approves or rejects the deployment, which is essential for production deployment control.
What should I do if I get this MLA-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.
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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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