AWS Certified Cloud Practitioner
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A product team wants an LLM to generate secure coding guidelines for internal developers. Zero-shot outputs contain outdated security patterns. Which prompting approach best ensures the model reflects modern best practices?
Real Exam
Community
RRitesh
A
Provide recent secure-code examples and ask the LLM to generalize (few-shot)
B
Ask the LLM to "think step-by-step" using chain-of-thought
C
Role-play as a "Security Auditor" without examples
D
Use sampling temperature of 1.2 for creativity
Explanation:
Explanation
The correct answer is A because:
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Few-shot learning with recent examples provides the LLM with concrete, up-to-date patterns of secure coding practices that it can learn from and generalize.
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Zero-shot outputs contain outdated security patterns - this indicates the model's base knowledge is not current enough, so providing recent examples directly addresses this gap.
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Chain-of-thought (B) helps with reasoning but doesn't necessarily provide the model with updated security knowledge.
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Role-playing (C) without examples relies on the model's existing knowledge, which is already outdated.
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Higher temperature (D) increases creativity/randomness but doesn't ensure modern security practices are followed.
Key takeaway: When dealing with domain-specific, time-sensitive knowledge like security best practices, providing recent examples (few-shot prompting) is the most effective way to guide the LLM toward current standards.
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