You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got `2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?

• A. Send a follow-up request including the validation error, asking the model to correct its output.
• B. Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.
• C. Pre-process source documents to standardize problematic formats before sending them for extraction.
• D. Set temperature to 0 to eliminate output variability and ensure consistent formatting.

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A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

• A. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
• B. First call get_customer and lookup_order to gather account context, then escalate to a human agent.
• C. Immediately call escalate_to_human with the conversation history.
• D. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.

설명: (Fast2test 회원만 볼 수 있음)

Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this instruction: "Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about." Developers confirm that some missed findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every finding tagged with metadata, and actionable. Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?

• A. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.
• B. Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.
• C. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.
• D. Add a second review pass that rereads the diff using the same prompt and looks for anything the first pass may have missed.

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A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

• A. Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.
• B. Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
• C. Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
• D. Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.

설명: (Fast2test 회원만 볼 수 있음)

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.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

• A. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
• B. Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
• C. Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
• D. Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

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