Using Amazon SageMaker Model Monitor, a company detects data drift that exceeds the configured threshold and wants to prevent harm to the model’s predictions. Which action should they take?

When Amazon SageMaker Model Monitor detects data drift exceeding a threshold, it indicates that the incoming data no longer aligns with the data the model was trained on. This discrepancy can degrade model performance and lead to inaccurate predictions. The most effective action to prevent harm and restore model accuracy is to re-train the model with fresh, representative data that reflects the current data distribution. Restarting the SageMaker AI endpoint will not address the underlying data drift issue; it only restarts the inference service. Adjusting monitoring sensitivity might temporarily silence alerts but doesn't fix the model's performance degradation. Setting up experiments tracking is useful for model development and comparison but doesn't resolve an active data drift problem affecting a deployed model.