CCAR-F Practice Questions

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, and Glob—and integrates with Model Context Protocol (MCP) servers.

After adding an MCP server with specialized code-refactoring tools—extract_function, rename_variable, and inline_function—you notice that the agent still uses basic text manipulation through Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy. Examining the configuration, you find that each MCP tool has a minimal description such as, “extract_function: Extracts a function from code.”

What is the most effective way to improve adoption of the MCP refactoring tools?

The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate—it generates messages such as, “I’ll ask the web-search agent to find sources on this topic”—but no subagent execution occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

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 automated review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as “not worth addressing.” Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.

Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?

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, and Glob—and integrates with Model Context Protocol (MCP) servers.

During testing, you observe that in extended exploration sessions lasting more than 30 minutes, the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they have already explored.

What is the most effective approach to address this?

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 system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like “a handful” or “a splash,” the model either invents specific amounts or leaves fields empty—accounting for 23% of all corrections.

How should you use this feedback to improve extraction accuracy?

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?

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, CLAUD

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You’re implementing a caching layer for API responses to speed up the /products endpoint. 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?

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.

You’ve configured your Claude agent with three MCP servers: one for git operations, one for Jira ticket management, and one for documentation search.

When a user asks the agent to “create a branch for JIRA-123 and add documentation links to the ticket,” how does the agent access tools across these servers?

You are building a structured data-extraction system using Claude. The system extracts information from unstructured documents, validates output against JSON schemas, and integrates the results with downstream systems.

Monitoring reveals that specifications sometimes appear inconsistently within source documents. For example, a summary section might state “Battery: 4000 mAh,” while the detailed specifications table states “Battery: 4200 mAh.” Your current schema contains a single battery_capacity field.

This inconsistency occurs in approximately 15% of documents, and historical analysis confirms that the detailed specifications table is accurate 90% of the time.

What is the most effective approach?

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.

The synthesis agent receives summarized findings from the web-search and document-analysis agents, then passes a consolidated summary to the report generator. During testing, you discover that the generated reports make factual claims without proper citations. The report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps.

What is the most effective approach to ensure proper source attribution in the final reports?