AWS Certified Cloud Practitioner

Explanation

Option A is correct because providing recent secure-code examples and asking the LLM to generalize (few-shot learning) is the most effective approach for improving code security. This method:

1. Provides concrete examples of secure coding patterns

2. Demonstrates real-world security practices that the LLM can learn from

3. Uses few-shot learning which is effective for teaching LLMs specific patterns

4. Generalizes from examples to apply security principles to new code

Why other options are less effective:

• Option B (Chain-of-thought): While asking the LLM to "think step-by-step" can improve reasoning, it doesn't specifically teach secure coding practices unless security considerations are explicitly included in the reasoning chain.

• Option C (Role-playing without examples): Simply role-playing as a security auditor without providing examples lacks concrete guidance on what constitutes secure code.

• Option D (High temperature for creativity): Using a higher sampling temperature (1.2) increases randomness and creativity, which could actually lead to less secure code as the LLM might generate unconventional or untested patterns.

Best Practice: The most effective approach combines few-shot learning with security-focused examples, as this directly teaches the LLM secure coding patterns through demonstration rather than relying on abstract reasoning or role-playing alone.