AWS Certified Cloud Practitioner

Explanation

Fine-tuning (Option B) is the best approach because:

• Large dataset availability: The company has "thousands of labeled examples" which is sufficient for fine-tuning
• Customization requirement: They need to "tailor an existing foundation model" to match their specific "brand tone"
• Amazon Bedrock support: Amazon Bedrock provides fine-tuning capabilities for foundation models

Why other options are less suitable:

• Reinforcement learning (A): Typically used for optimizing model behavior through reward systems, not for initial customization with labeled data
• Few-shot prompting (C): Uses a small number of examples (typically 2-10) in prompts, but the company has thousands of examples
• Zero-shot inference (D): Uses no examples, just instructions, which wouldn't leverage the available labeled dataset

Fine-tuning allows the model to learn from the extensive labeled dataset and adapt its output style to match the company's specific brand requirements.