An application frequently repeats the same background instructions in every API request. What is a potential downside?

• A. Higher token usage and cost.
• B. Reduced model capability.
• C. Lower reliability.
• D. Increased hallucinations only.

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 has three requirements for Claude Code's behavior in your project
1. Claude must never modify files in the db/migrations/ directory
2. Claude should prefer your custom logging module over console.log
3. All TypeScript files must be auto-formatted with Prettier after
every edit
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1. How should you restructure these requirements across Claude Code's configuration mechanisms?

• A. Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/,a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
• B. Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
• C. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook on the Edit tool that runs Prettier on changed TypeScript files.
• D. Rewrite all three requirements in CLAUDE.md using stronger directive language and add few- shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.

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.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate - it's forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows. What's the most effective approach to complete this investigation?

• A. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
• B. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
• C. Clear context with /clear, then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
• D. Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing," "trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.

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.
Your test generation produces unit tests for new code, but reviews show that 55% are low-value:
trivial assertions that only verify functions do not throw exceptions, tests duplicating existing coverage, or tests ignoring your team's fixture conventions.
How do you reduce the rate of low-value tests being generated in the first place?

• A. Implement two-phase generation in which a second Claude call scores each test against quality criteria, filtering out low-scoring tests before presenting results to developers.
• B. Document testing standards in CLAUDE.md, including valuable-test criteria, available fixtures and their intended use cases, and examples distinguishing meaningful behavioral tests from trivial assertions.
• C. Add post-generation coverage analysis that automatically filters out any generated test that does not increase line coverage beyond existing tests.
• D. Restrict test generation to directories where historical quality metrics show higher acceptance rates, disabling it for areas where generated tests consistently require substantial editing.

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.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

• A. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.
• B. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
• C. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
• D. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.