AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 143

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?

Answer options

• A. Use Amazon SageMaker Debugger to automatically send alerts when model performance anomalies are detected.
• B. Use AWS X-Ray to monitor the performance of the SageMaker AI endpoint and the incoming requests to inform model re-training.
• C. Use Amazon SageMaker Ground Truth to curate a high-quality dataset. Use the dataset to re-train the model.
• D. Use Amazon SageMaker Clarify to monitor model and feature attribution bias to inform model re-training.

Correct answer: D

Explanation

The correct answer is D because Amazon SageMaker Clarify helps in monitoring bias in the model's predictions, allowing for informed decisions on when to re-train the model. Option A focuses on alerting for anomalies, which does not directly address performance degradation. Option B provides monitoring but lacks the focus on bias and retraining. Option C, while useful for dataset quality, does not specifically address ongoing performance monitoring and re-training needs.