Answer: Negative prompting and content filtering

**Explanation:** Negative prompting and content filtering is the correct strategy for this scenario because: 1. **Negative Prompting**: This technique involves explicitly telling the AI model what NOT to generate. By specifying that competitor brand names and offensive language should be excluded, the model learns to avoid these elements in its outputs. 2. **Content Filtering**: This involves implementing filters or guardrails that automatically detect and remove unwanted content from the AI-generated output. AWS Bedrock provides built-in content filtering capabilities that can be configured to block specific types of content. 3. **Why other options are incorrect**: - **Zero-shot prompting (A)**: This refers to providing a prompt without any examples, which doesn't specifically address content filtering requirements. - **Reinforcement learning (C)**: This is a training methodology where models learn through trial and error with rewards, not a content filtering strategy. - **Hyperparameter tuning (D)**: This involves adjusting model parameters to optimize performance, not specifically for content filtering. 4. **AWS Bedrock Context**: AWS Bedrock offers built-in safety features and content filters that can be configured to block specific types of content, making negative prompting and content filtering the most appropriate approach for ensuring brand safety and compliance in marketing content generation.

Author: Ritesh Yadav