hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: Using an AWS Step Functions state machine to…
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.)
⚠ Common exam trap
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.
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
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.
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.
- ✓
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.
- ✗
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.
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 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.