A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon...

Explaining how a model makes predictions is the domain of model interpretability and explainability. Amazon SageMaker Clarify is designed specifically to provide explanations for ML predictions using techniques such as SHAP (SHapley Additive exPlanations).

SageMaker Clarify can analyze deployed endpoints to show feature importance, explain individual predictions, and quantify how each input feature contributes to the model’s output. This makes it ideal for communicating model behavior to non-technical stakeholders and meeting transparency requirements.

Model Monitor focuses on data and performance drift, not explanations. A/B testing and shadow endpoints compare performance but do not explain predictions.

Therefore, SageMaker Clarify is the correct solution for explaining model predictions.