Answer: To simplify data preparation, transformation, and feature engineering

Amazon SageMaker Data Wrangler is specifically designed to simplify and accelerate data preparation and feature engineering tasks for machine learning workflows. It provides a visual interface for data preparation, allowing users to import, transform, and engineer features without writing extensive code. **Key functions of SageMaker Data Wrangler:** - **Data Import**: Connect to various data sources (S3, Athena, Redshift, etc.) - **Data Preparation**: Clean, transform, and prepare data for ML - **Feature Engineering**: Create new features from existing data - **Data Quality Analysis**: Identify data quality issues and patterns - **Export to ML Pipelines**: Generate code for SageMaker Pipelines **Why other options are incorrect:** - **Option A**: Analyzing model performance metrics is handled by SageMaker Model Monitor and other evaluation tools - **Option C**: Tracking and comparing ML experiments is the function of SageMaker Experiments - **Option D**: While Data Wrangler can help prepare feature pipelines, its main function is data preparation, not specifically deployment to SageMaker Studio

Author: Ritesh Yadav