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
Both are IaC services that can define and provision SageMaker resources in a reproducible manner.
- ✗
SageMaker Pipelines
Why it's wrong here
Pipelines orchestrate ML workflows but are not IaC tools for infrastructure provisioning.
- ✗
AWS Step Functions
Why it's wrong here
Step Functions orchestrate workflows but are not designed for defining infrastructure.
- ✗
SageMaker Studio
Why it's wrong here
Studio is an IDE, not an IaC tool.
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