Free Amazon MLS-C01 Actual Exam Questions - Question 2 Discussion
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?
C. SageMaker Clarify is designed exactly for explanation reports with minimal setup, unlike Lambda or CloudWatch which need more manual work. Even if attaching isn’t automatic, it’s still the simplest overall.
I’m thinking B could be tricky since Lambda doesn’t automatically create the reports for each prediction without extra work. So probably not the least effort. C sounds simpler overall. C
C/B? Clarify definitely handles explanations well and integrates smoothly with SageMaker. Lambda (B) could work but needs more setup for generating those plots and reports manually, so more effort involved.
It’s C. SageMaker Clarify is designed specifically for explainability and bias detection, so it can generate insights with minimal extra coding. Options A and D are off since Model Debugger focuses on debugging training issues, not explanations, and CloudWatch metrics aren’t intended for detailed explanation reports. Lambda in B feels like extra work since you’d have to build your own explanation logic and visualization from scratch. Clarify integrates well with SageMaker, making it the smoothest choice here.
C/D? Not sure if SageMaker Clarify (C) automatically attaches reports or if custom CloudWatch metrics (D) are even meant for explanations. Anyone knows how seamless Clarify is here?