MLA-C01 · Question #163
An ecommerce company trains an ML model to forecast demand for near real-time inventory management based on historical customer activity. The company successfully deploys the trained model to a…
The correct answer is D. Use Amazon SageMaker Clarify to monitor model and feature attribution bias to inform model re-. Amazon SageMaker Clarify can continuously monitor models in production for bias and feature attribution changes over time, which are indicators of data drift. This enables an automated, long- term approach to detecting degradation and informing when the model should be retrained.
Question
An ecommerce company trains an ML model to forecast demand for near real-time inventory management based on historical customer activity. The company successfully deploys the trained model to a production Amazon SageMaker AI endpoint. However, the company notices that the model’s forecast performance degrades over time. The company needs a long-term and automated solution to mitigate the performance degradation. Which solution will meet these requirements?
Options
- AUse Amazon SageMaker Debugger to automatically send alerts when model performance
- BUse AWS X-Ray to monitor the performance of the SageMaker AI endpoint and the incoming
- CUse Amazon SageMaker Ground Truth to curate a high-quality dataset. Use the dataset to re-
- DUse Amazon SageMaker Clarify to monitor model and feature attribution bias to inform model re-
How the community answered
(45 responses)- A4% (2)
- B9% (4)
- C2% (1)
- D84% (38)
Explanation
Amazon SageMaker Clarify can continuously monitor models in production for bias and feature attribution changes over time, which are indicators of data drift. This enables an automated, long- term approach to detecting degradation and informing when the model should be retrained.
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