Free Anthropic CCAR-F Exam Questions
Absolute Free CCAR-F Exam Practice for Comprehensive Preparation
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Anthropic CCAR-F Exam Questions
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Provided By: Anthropic
- Exam: Claude Certified Architect - Foundations
- Certification: Anthropic Claude Certified Architect - Foundations
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Total Questions: 152
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Updated On: Sep 22, 2026
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Rated: 4.9 |
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Online Users: 304
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Question 1
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Your code-review prompts include both implementation changes and the corresponding test file, but the
review comments fail to identify untested code paths. The model correctly flags functions that have no tests at
all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack
coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the
pipeline?
Answer: B
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Your code-review prompts include both implementation changes and the corresponding test file, but the
review comments fail to identify untested code paths. The model correctly flags functions that have no tests at
all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack
coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the
pipeline?
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Question 2
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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 reviewer uses a single prompt covering security issues, API design, and business-logic
correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for
business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the
prompt, logic recall improves to 41%, but API-design recall drops to 68%.
How should you address this trade-off to improve detection across both categories?
Answer: A
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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 reviewer uses a single prompt covering security issues, API design, and business-logic
correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for
business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the
prompt, logic recall improves to 41%, but API-design recall drops to 68%.
How should you address this trade-off to improve detection across both categories?
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Question 3
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You are building a structured data extraction system using Claude. The system extracts information fromunstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, andmaintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.Monitoring shows 12% of extractions fail Pydantic validation with specific errors like “expected float forquantity, got ‘2 to 3’”. Retrying these requests without modification produces identical failures.What’s the most effective approach to recover from these validation failures?
Answer: A
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You are building a structured data extraction system using Claude. The system extracts information fromunstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, andmaintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.Monitoring shows 12% of extractions fail Pydantic validation with specific errors like “expected float forquantity, got ‘2 to 3’”. Retrying these requests without modification produces identical failures.What’s the most effective approach to recover from these validation failures?
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Question 4
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In production, you observe that simple fact-checking queries—for example, “What year was the Paris Climate
Agreement signed?”—traverse all four subagents sequentially, consuming more than 40 seconds and
significant tokens per query. Complex comparative research benefits from the full pipeline. Your query
distribution is diverse and evolving as users discover new applications. What is the most effective approach to
optimize for varying query complexity?
Answer: C
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In production, you observe that simple fact-checking queries—for example, “What year was the Paris Climate
Agreement signed?”—traverse all four subagents sequentially, consuming more than 40 seconds and
significant tokens per query. Complex comparative research benefits from the full pipeline. Your query
distribution is diverse and evolving as users discover new applications. What is the most effective approach to
optimize for varying query complexity?
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Question 5
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You are building a customer support resolution agent using the Claude Agent SDK. The agent handles highambiguity requests like returns, billing disputes, and account issues. It has access to your backend systemsthrough 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.Your process_refund tool returns two types of errors: technical errors (“503 Service Unavailable”,“Connection timeout”) that are transient (~5% of calls), and business errors (“Order exceeds 30-day returnwindow”, “Item already refunded”) that are permanent (~12% of calls). Monitoring shows the agent wastes 3–4 turns retrying business errors that can never succeed. Currently, both error types return only a plain textmessage to Claude.What’s the most effective way to reduce wasted retries while improving customer-facing response quality?
Answer: D
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You are building a customer support resolution agent using the Claude Agent SDK. The agent handles highambiguity requests like returns, billing disputes, and account issues. It has access to your backend systemsthrough 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.Your process_refund tool returns two types of errors: technical errors (“503 Service Unavailable”,“Connection timeout”) that are transient (~5% of calls), and business errors (“Order exceeds 30-day returnwindow”, “Item already refunded”) that are permanent (~12% of calls). Monitoring shows the agent wastes 3–4 turns retrying business errors that can never succeed. Currently, both error types return only a plain textmessage to Claude.What’s the most effective way to reduce wasted retries while improving customer-facing response quality?