Answer: To provide an integrated development environment (IDE) for building, training, and debugging ML models

Amazon SageMaker Studio is an integrated development environment (IDE) for machine learning that provides a single web-based visual interface where you can perform all machine learning development steps. It allows data scientists and developers to prepare data, build, train, debug, deploy, and monitor machine learning models in one place. **Key features of Amazon SageMaker Studio:** - Integrated Jupyter notebooks - Experiment management and tracking - Model debugging and profiling - Model deployment and monitoring - Collaboration tools - Integration with other AWS services **Why other options are incorrect:** - **Option A**: While SageMaker can automate GPU provisioning, this is not its primary function. GPU provisioning is a feature within the broader SageMaker ecosystem. - **Option C**: Creating embeddings for RAG (Retrieval-Augmented Generation) systems is a specific use case that can be done within SageMaker, but it's not the primary function of SageMaker Studio. - **Option D**: Hosting static websites is typically done with services like Amazon S3 and CloudFront, not SageMaker Studio.

Author: Ritesh Yadav