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You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools--Read, Write, Bash, Grep, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

• A. Read the project's main entry file and follow import statements to trace where eval() might be used.
• B. Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.
• C. Use Grep to search for the regular-expression pattern eval\( across all files in the codebase.
• D. Use Bash to run ls -R | grep eval and search the recursively listed filenames.

A company is building its first production Claude application. Which principle should guide the initial deployment?

• A. Maximize prompt complexity immediately.
• B. Start with a simple design and iteratively evaluate improvements.
• C. Optimize only for response speed.
• D. Disable monitoring to reduce costs.

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation--for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field. Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?

• A. Have the model output field-level confidence scores, and then calibrate review thresholds using a labeled validation set.
• B. Prioritize the review of all extractions where required fields are empty or explicitly marked as not found.
• C. Randomly sample 20% of extractions for review, using corrections to track accuracy and identify error patterns.
• D. Review all extractions from documents with formatting anomalies, such as unusual layouts or mixed content types.

Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?

• A. Add a confirmation step that requires users to type "CONFIRM DELETE" before delete_file executes.
• B. Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.
• C. Add few-shot examples to the system prompt demonstrating that requests involving "backup" or
  "old" should use archive_file.
• D. Expand tool descriptions to clarify use cases, adding guidance like "Do not use for backup files" to delete_file.

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.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

• A. Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
• B. Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
• C. Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
• D. Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.