An ML engineer deployed a sentiment-analysis model to a SageMaker endpoint and must provide stakeholders with explanations of how the model makes predictions. Which solution will produce explanations for the model's outputs?

SageMaker Clarify provides tools for bias detection and explainability for machine learning models. It can generate various types of explanations, such as SHAP (SHapley Additive exPlanations) values, to help understand how features contribute to a model's predictions. This directly addresses the requirement to provide stakeholders with explanations of model outputs. Using SageMaker Model Monitor tracks data quality, model quality, and bias drift over time, but it does not generate explanations for individual predictions. Showing the distribution of inference results from A/B testing in CloudWatch helps compare model performance but doesn't explain why a specific prediction was made. Creating a shadow endpoint and comparing prediction differences is a method for testing new model versions or configurations, not for generating explanations of an existing model's outputs.