Answer: Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.

**Explanation:** Amazon SageMaker Clarify is specifically designed to provide machine learning model explainability and bias detection capabilities. For a medical company customizing a foundation model for diagnostic purposes, regulatory requirements typically demand transparency and explainability to understand how the model makes decisions. **Why option B is correct:** - **Amazon SageMaker Clarify** provides tools to explain model predictions by generating feature importance scores - It can detect bias in data and model predictions - It generates reports and metrics that help understand model behavior - This transparency is crucial for regulatory compliance in healthcare applications **Why other options are incorrect:** - **Option A (Amazon Inspector)**: This is a security vulnerability assessment service, not for model explainability - **Option C (Amazon Macie)**: This is a data security and privacy service for discovering and protecting sensitive data, not for model transparency - **Option D (Amazon Rekognition)**: This is a computer vision service for image and video analysis, not for model explainability **Key AWS Services for ML Explainability:** 1. **Amazon SageMaker Clarify** - Primary service for ML model explainability and bias detection 2. **Amazon SageMaker Model Monitor** - For monitoring model performance and data quality 3. **Amazon SageMaker Debugger** - For debugging and profiling ML models For healthcare applications requiring regulatory compliance, model explainability is critical to ensure patient safety and meet standards like HIPAA, FDA regulations, or other medical device regulations.

Author: Ritesh Yadav