Free Anthropic CCAR-F Exam Practice Questions
Claude Certified Architect - Foundations
Total Questions: 152Focus Only on What Matters For Anthropic CCAR-F Exam Preparation
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Anthropic CCAR-F Exam Sample Questions & Answers
Your automated code review is missing genuine bugs in pull requests. Investigation reveals that the review prompt includes this instruction: ''Only flag critical issues that would definitely cause production failures. Ignore minor concerns and anything you are uncertain about.'' Developers confirm that some missed findings are genuine logic errors that the model investigated but chose not to report. The team requires the review output to remain structured, with every finding tagged with metadata, and actionable. Which prompt change both removes the cause of the suppressed findings and preserves structured, tagged output for downstream filtering?
Production monitoring shows that follow-up queries such as ''summarize what we learned about market trends'' consistently take more than 40 seconds. Investigation reveals that the coordinator spawns the synthesis subagent for every summarization request, passing more than 80,000 tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research. What is the most effective way to improve response time for these follow-up summaries?
After the web-search and document-analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?
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 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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues: (1) compound phrases like ''Python and SQL'' are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says ''Extract all skills mentioned.''
What's the most effective improvement?
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