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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 96 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 96
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company has an ML model that uses historical transaction data to predict customer behavior. An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics.

Which solution will provide this level of analysis?

  • A. Use Amazon CloudWatch to monitor network metrics and CPU metrics for resource optimization during model training.
  • B. Create AWS Glue DataBrew recipes to correct the data based on statistics from the model output.
  • C. Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.
  • D. Create AWS Lambda functions to automate data pre-processing and to ensure consistent quality of input data for the model.
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Suggested Answer: C 🗳️
by chris_spencer at March 11, 2025, 2:11 p.m.
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hdomingo
6 days, 5 hours ago
Selected Answer: C
Option C - SageMaker Clarify is built for bias detection and explainability.   It can analyze training data and model predictions to identify potential biases.   It provides insights into how different demographic groups are affected by the model.
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