A SageMaker pipeline that includes Model Monitor reports baseline_drift_check violations in the MonitoringExecution output, causing the pipeline to fail. What is the appropriate action to address these drift violations?

When Model Monitor detects baselinedriftcheck violations, it indicates that the current model's performance or data characteristics have significantly diverged from its original training conditions. The most effective solution is to retrain the model with updated, representative training data. This ensures the model learns from the most recent patterns and relationships. Simultaneously, a new baseline must be created and used in Model Monitor. This new baseline reflects the characteristics of the newly trained model and its updated training data, providing a relevant reference point for future drift detection. Retraining the model and inspecting with SageMaker Debugger is a good step but doesn't address the baseline drift itself. Retraining with new data but using the original baseline will likely lead to continued drift violations because the baseline is outdated. Rerunning the pipeline with emitmetrics enabled only changes logging behavior and does not resolve the underlying data or model drift issue.