AWS Certified Cloud Practitioner

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.