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, Glob) and integrates with Model Context Protocol (MCP) servers.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today.
The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge. What approach best balances efficiency and accuracy?

• A. Start fresh session to ensure the agent works with current codebase state without stale assumptions
• B. Resume the session and inform the agent which specific files changed for targeted re-analysis
• C. Resume the session and immediately have the agent re-read all 12 previously analyzed files
• D. Resume the session without informing the agent about the changed files

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.
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average 18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your linter in CI, (2) findings on automatically generated template code under src/gen/, and (3) rendering- helper patterns that are intentional project conventions but get flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs.
What is the most effective way to reduce this noise while preserving the detection of genuine issues?

• A. Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.
• B. Add detailed explanations to the project's CLAUDE.md describing which patterns are intentional, that linting is handled separately by CI, and that the src/gen/ directory contains automatically generated template code.
• C. Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and apply stricter evidence requirements to rendering-related issues.

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

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

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 implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but
5% of extractions still contain empty arrays or null values for required fields such as citations and methodology. Spot-checking reveals that the source documents contain this information, but in varied formats--inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?

• A. Modify the schema to make citations and methodology optional, and flag incomplete records for manual review instead of failing validation.
• B. Build a regex-based post-processing layer that scans source documents for citation patterns and methodology keywords, populating empty fields when the model fails to extract them.
• C. Implement retry logic that resends requests when validation detects empty required fields.
• D. Add few-shot examples demonstrating extractions from documents with varied structures, showing how to identify citations in different formats and locate methodology details across section types.

Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

• A. Return is_error: true with the original message content echoed back.
• B. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
  Please retry the notification."
• C. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
  Message may have been sent. Avoid retry."
• D. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.