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MLA-C01 Practice Question: Automate its machine learning pipeline using AWS…
A company wants to automate its machine learning pipeline using AWS CodePipeline and Amazon SageMaker. The pipeline should train a model, evaluate it, and if the evaluation passes, register the model in the SageMaker Model Registry. Which service should the company use to orchestrate the training and evaluation steps?
⚠ Common exam trap
Many exam-takers confuse AWS Step Functions (a general-purpose orchestrator) with SageMaker Pipelines (a specialized ML orchestrator), overlooking that SageMaker Pipelines provides built-in SageMaker step types and native Model Registry integration, which Step Functions lacks without custom Lambda functions.
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
✓
Amazon SageMaker Pipelines
Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for creating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services.
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 CodePipeline
Why it's wrong here
CodePipeline orchestrates build and deploy stages, not iterative SageMaker training and evaluation logic. It cannot natively branch on evaluation metrics to conditionally register a model. Step Functions, with SageMaker and Lambda integrations, handles that conditional workflow; CodePipeline suits CI/CD release pipelines instead.
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AWS Glue Workflows
Why it's wrong here
Glue Workflows orchestrate extract, transform and load jobs within AWS Glue; they cannot invoke SageMaker training jobs or branch on model evaluation metrics. Step Functions provides that conditional orchestration. Glue Workflows would be correct for sequencing crawlers and ETL jobs, not ML training pipelines.
- ✗
AWS Step Functions
Why it's wrong here
CodePipeline orchestrates build and deploy stages, not iterative train-evaluate-register branching with conditional retries; Step Functions' state machine handles that. Step Functions is tempting because it genuinely orchestrates multi-step workflows, and would be correct if the pipeline needed complex branching logic outside CodePipeline's linear stage model.
- ✓
Amazon SageMaker Pipelines
Why this is correct
Amazon SageMaker Pipelines provides native orchestration of training, evaluation and conditional model-registration steps, satisfying the requirement to register only when evaluation passes. Its ConditionStep gates registration on the evaluation metric, while CodePipeline triggers the pipeline rather than orchestrating ML steps. This integrates directly with the SageMaker Model Registry.
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