AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 5

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?

Answer options

• A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
• B. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
• C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
• D. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Correct answer: C

Explanation

The correct answer is C because SageMaker Pipelines provides the necessary orchestration capabilities and integration with SageMaker Studio for managing ML workflows, while SageMaker ML Lineage Tracking ensures comprehensive tracking for auditing and compliance. Options A and B incorrectly suggest using AWS CodePipeline, which does not offer the same level of control and visualization as SageMaker Pipelines. Option D does not leverage the ML Lineage Tracking feature, which is essential for maintaining a running history of experiments.