CCA-F: Claude Certified Architect — Foundations

Question

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system performs automated code reviews, creates test cases, and gives feedback on pull requests. You need to design prompts that deliver actionable feedback while minimizing false positives.

After deploying automated code review, developers report that roughly 35% of flagged findings are false positives with recurring patterns: style recommendations conflict with team conventions, security warnings concern patterns that are safe in your deployment context, and performance recommendations would harm your particular use case. You want to lower false positives while retaining the ability to identify genuine issues.

Which approach best allows the model to generalize its judgment to new code patterns it has not encountered before?

Explanation

Few-shot examples with annotated acceptable and problematic code teach the model the contextual decision boundary for each review category. Diverse, relevant examples improve accuracy and consistency, enabling the model to apply the learned distinctions to analogous unseen patterns rather than merely suppressing broad categories of findings.

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CCA-F: Claude Certified Architect — Foundations

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