Answer: Model Customization (Fine-tuning)

## Explanation **Model Customization (Fine-tuning) is the correct approach** because: - **Fine-tuning** allows the Titan model to be trained on the brand's specific product data and marketing tone - This process adapts the base model to understand and generate content that aligns with the brand's unique requirements - The model learns from the custom dataset and can produce outputs that reflect the brand's specific voice and product information **Why other options are incorrect:** - **A) Knowledge Bases for Amazon Bedrock**: This is for retrieval-augmented generation (RAG) where you provide external data sources, but doesn't customize the model's fundamental behavior - **C) Guardrails for Bedrock**: This is for content filtering and safety controls, not for adapting the model to specific data and tone - **D) Watermark Detection**: This is for identifying AI-generated content, not for model customization Fine-tuning is specifically designed to make foundational models like Titan work better with domain-specific data and adapt to particular writing styles or tones.

Author: Ritesh Yadav