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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 95 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 95
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company deployed an ML model that uses the XGBoost algorithm to predict product failures. The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating data. An AWS Lambda function provides the predictions to the company's application.

An ML engineer must implement a solution that uses incoming live data to detect decreased model accuracy over time.

Which solution will meet these requirements?

  • A. Use Amazon CloudWatch to create a dashboard that monitors real-time inference data and model predictions. Use the dashboard to detect drift.
  • B. Modify the Lambda function to calculate model drift by using real-time inference data and model predictions. Program the Lambda function to send alerts.
  • C. Schedule a monitoring job in SageMaker Model Monitor. Use the job to detect drift by analyzing the live data against a baseline of the training data statistics and constraints.
  • D. Schedule a monitoring job in SageMaker Debugger. Use the job to detect drift by analyzing the live data against a baseline of the training data statistics and constraints.
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Suggested Answer: C 🗳️
by chris_spencer at March 11, 2025, 2:07 p.m.
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AgboolaKun
2 weeks, 3 days ago
Selected Answer: C
The correct answer is C: To detect decreased model accuracy over time using incoming live data, the ML engineer should schedule a monitoring job in SageMaker Model Monitor. This monitoring job will detect drift by analyzing the live data against a baseline of the training data statistics and constraints. SageMaker Model Monitor is specifically designed for this purpose, automatically comparing production data against the baseline, and can alert when deviations occur that might indicate decreased model accuracy.
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