The company requires its models to be transparent and explainable. Which two Amazon SageMaker features provide capabilities that help achieve transparency and explainability? (Choose two.)

SageMaker Model Cards provide a centralized repository for documenting model details, including purpose, training data, performance metrics, and ethical considerations, directly contributing to transparency and explainability by making model information accessible. SageMaker Clarity helps explain model predictions by generating insights into feature importance and model behavior, making complex models more understandable. SageMaker Pipelines orchestrates machine learning workflows but doesn't directly provide transparency or explainability features. SageMaker Model Monitor detects data and model drift but doesn't explain individual predictions or model internals. SageMaker Debugger helps identify and fix issues during model training but doesn't focus on post-training explainability.