MLS-C01 Practice Question: Machine Learning Implementation and Operations
You are building a CI/CD pipeline for SageMaker using AWS CodePipeline. Which THREE components are essential for a fully automated model training and deployment pipeline?
⚠ Common exam trap
Watch out — candidates often assume AWS CodeDeploy is the standard deployment service for all AWS resources, but SageMaker endpoints require the SageMaker API, making a Lambda function the correct choice for endpoint updates.
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 CodeCommit to store the training script and model code
AWS CodeCommit is essential because it provides a secure, scalable Git repository to store the training script and model code, which serves as the source stage in the CI/CD pipeline. This ensures version control and triggers the pipeline automatically on code changes, enabling a fully automated workflow.
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 CodeCommit to store the training script and model code
Why this is correct
Source control is essential for CI/CD.
- ✓
AWS CodeBuild to run the training job as a build step
Why this is correct
CodeBuild can execute SageMaker API calls.
- ✓
AWS Lambda function to create or update the SageMaker endpoint
Why this is correct
Lambda can be used to deploy the model.
- ✗
AWS CodeDeploy to deploy the model to an endpoint
Why it's wrong here
CodeDeploy does not support SageMaker endpoints natively.
- ✗
AWS CloudFormation to define the infrastructure
Why it's wrong here
Infrastructure as code is optional, not essential for pipeline.
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