Anthropic : CCAR-F Questions & Answers

After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?

• A. Implement post-processing that uses keyword matching to filter out findings containing terms such as "convention," "context-dependent," or "trade-off."
• B. Create a comprehensive written specification of all patterns that should not be flagged, and then include the full documentation in the system prompt.
• C. Add instructions to your system prompt to "be conservative," "only flag definite issues," and
  "consider that some patterns may be intentional."
• D. Include few-shot examples in your prompt showing annotated code snippets that distinguish acceptable patterns from genuine issues in each category.

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A developer wants Claude to explain every reasoning step internally before answering. Why might this request be inappropriate?

• A. Claude requires GPU acceleration.
• B. Internal reasoning should not be relied upon as an application feature.
• C. Temperature prevents explanations.
• D. JSON cannot contain reasoning.

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You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

• A. Add both servers to the project-level .mcp.json file
• B. Add venue server to .mcp.json and playlist server to ~/.claude.json.
• C. Add both servers to your local ~/.claude.json
• D. Add venue server to ~/.claude.json and playlist server to .mcp.json

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After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?

• A. Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.
• B. Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.
• C. Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.
• D. Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.

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The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

• A. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
• B. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
• C. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes
• D. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent

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