MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to deploy a machine learning model using infrastructure as code to ensure reproducibility. They need to define the SageMaker Studio domain, user profiles, and the endpoint configuration. Which tool should they use?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Pipelines (a CI/CD service for ML steps) with infrastructure-as-code tools, forgetting that Pipelines does not manage underlying infrastructure resources like Studio domains or endpoint configurations.
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
✓
AWS CloudFormation or AWS CDK
AWS CloudFormation and AWS CDK are infrastructure-as-code (IaC) tools that allow you to define, provision, and manage AWS resources declaratively. For this use case, they can model the entire SageMaker Studio domain, user profiles, and endpoint configuration in templates or code, ensuring reproducibility and version control. This aligns directly with the requirement to deploy ML infrastructure as code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS CloudFormation or AWS CDK
Why this is correct
AWS CloudFormation and AWS CDK both declare SageMaker resources — Studio domains, user profiles, endpoint configurations — as versioned templates, so the same stack redeploys identically across environments. This satisfies the reproducibility constraint, unlike console-based provisioning or imperative scripts, which drift and cannot be diffed or rolled back.
- ✗
SageMaker Pipelines
Why it's wrong here
SageMaker Pipelines orchestrates ML workflows such as training, processing and model registration; it does not declare Studio domains, user profiles or endpoint configuration as infrastructure resources. It is tempting because pipelines support reproducibility, so this would be correct if the requirement were automating a training workflow rather than provisioning SageMaker infrastructure declaratively.
- ✗
AWS Step Functions
Why it's wrong here
Step Functions coordinates application workflows through state machines, not infrastructure resource declarations; it cannot define a Studio domain, user profiles or endpoint configuration. It is tempting because it automates multi-step processes, so this would be correct if the requirement were orchestrating a deployment sequence rather than expressing infrastructure as code for reproducibility.
- ✗
SageMaker Studio
Why it's wrong here
SageMaker Studio is the integrated development environment where data scientists build and run notebooks; it is the resource being provisioned, not a tool for declaring domains, user profiles and endpoints as code. It is tempting because Studio hosts the work, and would be correct if the question asked where to develop models rather than how to define infrastructure reproducibly.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.