Answer: Model Customization (Fine-tuning)

## Explanation **Correct Answer: B) Model Customization (Fine-tuning)** **Why this is correct:** 1. **Fine-tuning** allows the Titan model to be trained on the e-commerce brand's specific product data and marketing tone, enabling it to generate responses that align with their brand voice and product information. 2. This approach adapts the base model to the company's specific domain and style requirements. 3. Fine-tuning modifies the model's weights to better understand and generate content based on the provided training data. **Why other options are incorrect:** - **A) Knowledge Bases for Amazon Bedrock**: This is for connecting models to external data sources for retrieval-augmented generation (RAG), but doesn't fundamentally change the model's behavior or tone. - **C) Guardrails for Bedrock**: This is for implementing safety controls and content filters, not for customizing the model's tone or product knowledge. - **D) Watermark Detection**: This is for identifying AI-generated content, not for customizing model behavior. **Key Takeaway:** When a company needs a foundation model to adopt their specific data, tone, and style, model customization through fine-tuning is the appropriate approach.

Author: Jin H