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.JSON cannot contain reasoning.
• D.Temperature prevents explanations.

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You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
During initial testing of the automated review pipeline, you notice that reviews on large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8?12 per run because of extensive agentic loops. Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort once it reaches both a fixed iteration count and a fixed dollar amount, enforced by Claude Code itself rather than by the surrounding job runner.
Which configuration change directly enforces both per-invocation caps?

• A.Switch the --model flag to a smaller, less expensive model so each iteration uses fewer tokens and has a lower per-call cost.
• B.Set timeout-minutes: 5 on the GitHub Actions job step and monitor per-run costs through the Anthropic Console usage dashboard.
• C.Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap iterations and spending.
• D.Set --permission-mode dontAsk to automatically deny tool-permission requests not included in the explicitly allowed set.

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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.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

• A.Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
• B.Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.
• C.Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.
• D.Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.

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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.
Your system must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?

• A.Pre-fill Claude's response with an opening brace to force JSON output, then complete and parse the response.
• B.Append instructions like "Output only valid JSON matching the schema exactly" and implement retry logic to re-prompt when JSON parsing fails.
• C.Define a tool with your target schema as input parameters and have Claude call it with the extracted data.
• D.Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude's text response as JSON.

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Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

• A.Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
• B.Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.
• C.Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.
• D.Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.

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