Answer: Model fine-tuning on custom datasets

## Explanation Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies. When a company wants to personalize models using their own historical data, they need to use **model fine-tuning** capabilities. **Why option B is correct:** - **Model fine-tuning on custom datasets** allows organizations to train foundation models on their proprietary data - This enables the model to learn patterns specific to the company's historical customer data - Fine-tuning creates a customized version of the foundation model that understands the company's specific domain and use cases **Why other options are incorrect:** - **A. Model evaluation reports**: These are used to assess model performance but don't enable learning from custom data - **C. Bedrock API Gateway integration**: This is about API management and access control, not model customization - **D. Real-time logs in CloudWatch**: These provide monitoring and logging capabilities but don't enable model training on custom data **Key Concept:** Fine-tuning allows foundation models to be adapted to specific domains by training them on custom datasets, which is essential for personalized recommendations based on historical customer data.

Author: Ritesh Yadav