MLA-C01 · Question #82
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability…
The correct answer is C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML. SageMaker Pipelines is AWS's native ML workflow orchestration service, purpose-built to provide fine-grained control over ML workflows and automatically renders those workflows as a DAG - making C the only option that satisfies the DAG visualization requirement. Its native…
Question
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. Which solution will meet these requirements?
Options
- AUse AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML
- BUse AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML
- CUse SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML
- DUse SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire
How the community answered
(47 responses)- A26% (12)
- B4% (2)
- C60% (28)
- D11% (5)
Explanation
SageMaker Pipelines is AWS's native ML workflow orchestration service, purpose-built to provide fine-grained control over ML workflows and automatically renders those workflows as a DAG - making C the only option that satisfies the DAG visualization requirement. Its native integration with SageMaker Studio provides the visual DAG interface, experiment tracking, model registry, and lineage tracking needed for governance, auditing, and compliance.
Why the distractors fail:
- A & B - AWS CodePipeline is a CI/CD tool for software deployment pipelines, not ML workflow orchestration; it lacks native DAG visualization and ML-specific governance features.
- D - SageMaker Experiments is a tracking/logging service for experiment metadata; it does not orchestrate workflows or render DAGs. The integration that provides the visual DAG and governance UI is Studio, not Experiments. Experiments is a component within the Studio ecosystem, but the orchestration+visualization combo requires Pipelines + Studio together.
Memory tip: Think "Pipelines = orchestrate + visualize (DAG), Studio = the IDE that shows it all." If a question mentions DAG + governance + ML workflows, SageMaker Pipelines + Studio is the answer. CodePipeline is for code, not ML models.
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