AWS Certified Cloud Practitioner

Explanation

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

1. Large labeled dataset: The company has "thousands of labeled examples" which is ideal for fine-tuning. Fine-tuning requires substantial labeled data to adjust the model's weights to learn specific patterns.

2. Tailoring to specific domain: The company wants to align product descriptions with their brand tone, which requires the model to learn specific stylistic patterns that fine-tuning can capture.

3. Amazon Bedrock support: Amazon Bedrock supports fine-tuning of foundation models, allowing customers to customize models with their own data.

Why other options are not suitable:

• Reinforcement learning (A): Typically used for training models through reward-based feedback, not for adapting a foundation model to a specific writing style with labeled examples.

• Few-shot prompting (C): Uses a small number of examples in the prompt itself. While useful, it's less effective than fine-tuning when you have thousands of labeled examples.

• Zero-shot inference (D): Uses the model without any examples, which wouldn't capture the specific brand tone the company wants.

Fine-tuning allows the foundation model to learn from the company's specific examples and produce outputs that match their brand voice consistently, making it the most effective approach given their resources.