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Exam AWS Certified Machine Learning - Specialty topic 1 question 259 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 259
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

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?

  • 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.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by SandeepGun at June 17, 2023, 4:41 p.m.
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vkbajoria
10 months, 3 weeks ago
Selected Answer: C
SageMaker Clarify can do the work
upvoted 1 times
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endeesa
1 year, 2 months ago
Selected Answer: C
The model is trained already, so C. I imagine using a debugger in production is nuts
upvoted 1 times
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Mickey321
1 year, 5 months ago
Selected Answer: C
The solution that will meet these requirements with the least development effort is C. Using SageMaker Clarify to generate the explanation report, and attaching the report to the predicted results . This solution allows the financial services company to use SageMaker Clarify, a feature that provides machine learning (ML) model transparency and explainability, to generate the explanation report for each loan approval prediction. SageMaker Clarify can provide feature importance scores, which indicate how much each feature contributes to the prediction, and SHAP values, which measure how each feature affects the prediction compared to the average prediction . The company can use these metrics to generate and attach the explanation report that contains the reason for why the customer was approved or denied for a loan.
upvoted 2 times
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ADVIT
1 year, 7 months ago
C, SageMaker Clarify can give explanation Why.
upvoted 1 times
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SandeepGun
1 year, 8 months ago
Selected Answer: C
Sagemaker Clarity is better option
upvoted 1 times
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Community vote distribution
A (35%)
C (25%)
B (20%)
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