Question # 1

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer,lookup_order,process_refund,escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate. Production logs reveal inconsistent error handling: whenlookup_orderfails, the agent sometimes retries 5+ times (wasteful when the order ID doesn’t exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses:{"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What’s the most effective improvement?

A. Enhance error responses with structured metadata—includeerror_category(transient/validation/permission),isRetryableboolean, and a description of what caused the failure. 
B. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
C. Create ananalyze_errorMCP tool the agent calls after any failure to determine the error category and recommended action.
D. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.

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Question # 2

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. After integrating a local MCP server providing code analysis tools (analyze_dependencies,find_dead_code,calculate_complexity), you verify the server is healthy and tools appear in thetools/listresponse. However, you observe that the agent consistently uses Grep to search for import statements instead of callinganalyze_dependencies—even when users explicitly ask about “code dependencies.” Examining tool definitions reveals: MCPanalyze_dependencies– “Analyzes dependency graph” Built-in Grep – “Search file contents for a pattern using regular expressions.Returns matching lines with line numbers and surrounding context.” What’s the most effective approach to improve the agent’s selection of MCP tools?

A. Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
B. Expand MCP tool descriptions to detail capabilities and outputs—e.g., “Builds dependency graph showing direct imports, transitive dependencies, and cycles.”
C. Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
D. Splitanalyze_dependenciesinto granular tools (list_imports,resolve_transitive_deps,detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.

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Question # 3

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 add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn’t specified which modules to prioritize. How should the agent decompose this open-ended task? 

A. Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top-level directory regardless of code complexity or business importance.
B. Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered.
C. Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning. 
D. Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically. 

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Question # 4

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer,lookup_order,process_refund,escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate. During testing, you find that when a customer says “I need a refund for my recent purchase,” the agent callsprocess_refundimmediately—but populates the requiredorder_idparameter with a plausible-looking but fabricated value instead of first callinglookup_orderto retrieve the actual order ID. The refund call fails because the fabricated ID doesn’t exist. Which change directly addresses the root cause of the agent fabricating theorder_idvalue?

A. Update theprocess_refundtool description to explicitly state thatorder_idmust be obtained from a priorlookup_ordercall and must never be assumed or invented. 
B. Switchtool_choicefrom"auto"to"any"to force the agent to make a tool call on every turn.
C. Add server-side validation that checks whether theorder_idexists in your database before executing the refund, returning an error to the agent if not found. 
D. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude. 

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Question # 5

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’re implementing a caching layer for API responses to speed up the/productsendpoint. You have a rough idea—Redis with a 5-minute TTL—but you’re new to production caching and aren’t sure what other considerations a robust implementation requires. What’s the most effective way to start your iterative workflow?

A. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
B. Use plan mode to analyze the current/productsendpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
C. Start with a minimal request: “Add Redis caching to/productswith 5-minute TTL.” Add features and fix issues through follow-up prompts as problems surface during testing.
D. Write a specification with your known requirements and “TBD” markers for uncertain areas, having Claude propose solutions for each TBD as it implements.

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Question # 6

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’re implementing a new payment processing module that must follow your project’s established patterns for database transactions, error handling, and audit logging. You’ve identified three existing modules that exemplify these patterns:db_utils.py,error_handlers.py, andaudit_logger.py. This is a one-off integration task—these patterns are well-documented in your team wiki and don’t need additional project-level documentation. What’s the most effective approach? 

A. Use@references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
B. Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.
C. Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
D. Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.

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Question # 7

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 security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes:authenticate()now returns a Promise instead of accepting a callback, theUsertype has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules. What’s the most effective approach?

A. Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration. 
B. Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
C. Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.
D. Paste the migration guide’s breaking changes into your prompt and use direct execution to update all usages across the 45 files.

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Question # 8

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 extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume). What’s the most effective basis for selecting which extractions to route for human review?

A. Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
B. Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.
C. Route extractions for review only when downstream systems report data quality issues or processing failures
D. Randomly sample 5% of extractions for review.

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Question # 9

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer, lookup_order, process_refund, escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate. Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team’s goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

A. Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
B. Split the workflow into two sequential agent invocations—a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
C. Add orchestration-layer code that checks the agent’s outcome after each loop termination—if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
D. Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.

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Question # 10

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. 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.
B. 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. 
C. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
D. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.

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