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MLA-C01 · Question #82

MLA-C01 Question #82: Real Exam Question with Answer & Explanation

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Deployment and Orchestration of ML Workflows

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

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Topics

#SageMaker Pipelines#ML Workflow Orchestration#Model Governance#DAG Visualization
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