The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

• A. The verification tool is clearing the agent's internal state after each successful validation step.
• B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
• C. The conversation history isn't being passed in subsequent API requests.
• D. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.

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

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The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues:
(1) the subagent reports "insufficient results" instead of trying
alternative approaches when the specified searches fail,
(2) research quality drops for emerging topics that do not match
expected patterns, and
(3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?

• A. Specify research objectives and quality criteria--such as coverage breadth, source diversity, and recency--rather than prescribing procedural steps, allowing the subagent to determine its search strategy.
• B. Classify each topic as either "well-defined" or "exploratory" and use a different instruction style for each category.
• C. Add fallback directives requiring alternative query formulations whenever the specified searches produce fewer than a predetermined number of results.
• D. Remove procedural details entirely and delegate using simple goals such as "research this topic thoroughly," relying on the subagent's general capabilities.

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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, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer asks the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain- specific use (e.g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?

• A. Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.
• B. Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.
• C. Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
• D. Use Grep to search for the function's original name across the codebase.

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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.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

• A. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
• B. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.
• C. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
• D. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.

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