AWS Certified Machine Learning – Specialty — Question 248

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.

Which solution will meet these requirements with the LEAST development effort?

Answer options

• A. Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
• B. Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
• C. Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
• D. Use custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.

Correct answer: C

Explanation

The correct answer is C because SageMaker Clarify is specifically designed to provide explanations for machine learning model predictions, making it the most efficient choice for this requirement. Options A and B involve additional tools and processes that increase development effort, while option D does not directly address the need for generating explanatory reports effectively.