Claude Certified Architect Foundations CCAR-F Exam Questions

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?

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?

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? 

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?

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?