Free Anthropic CCAR-F Exam Questions

Question 1

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.During testing, you find that when a customer says “I need a refund for my recent purchase,” the agent callsprocess_refund immediately—but populates the required order_id parameter with a plausible-looking butfabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call failsbecause the fabricated ID doesn’t exist.Which change directly addresses the root cause of the agent fabricating the order_id value?

A : Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.

B : Switch tool_choice from " auto " to " any " to force the agent to make a tool call on every turn.

C : Add server-side validation that checks whether the order_id exists 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.

Answer: A

Question 2

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.The system needs to extract candidate information (name, contact details, skills, work experience, education)from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missingrequired fields or incorrect data types will cause downstream validation failures.What is the most reliable approach to ensure Claude’s output consistently matches the schema?

A : Parse Claude’s text response with regex patterns to extract JSON objects, using retry logic for malformed responses.

B : Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.

C : Make two separate API calls—first extracting information as text, then asking Claude to format that text as JSON.

D : Define a tool with an input schema matching your required JSON structure and extract the data from Claude’s tool_use response.

Answer: D

Question 3

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.During testing, you find that when a customer says “I need a refund for my recent purchase,” the agent callsprocess_refund immediately—but populates the required order_id parameter with a plausible-looking butfabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call failsbecause the fabricated ID doesn’t exist.Which change directly addresses the root cause of the agent fabricating the order_id value?

A : Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.

B : Switch tool_choice from " auto " to " any " to force the agent to make a tool call on every turn.

C : Add server-side validation that checks whether the order_id exists 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.

Answer: A

Question 4

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?

A : Send a follow-up request including the validation error, asking the model to correct its output.

B : Set temperature to 0 to eliminate output variability and ensure consistent formatting.

C : Pre-process source documents to standardize problematic formats before sending them for extraction.

D : Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.

Answer: A

Question 5

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.During testing, you find that when a customer says “I need a refund for my recent purchase,” the agent callsprocess_refund immediately—but populates the required order_id parameter with a plausible-looking butfabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call failsbecause the fabricated ID doesn’t exist.Which change directly addresses the root cause of the agent fabricating the order_id value?

A : Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.

B : Switch tool_choice from " auto " to " any " to force the agent to make a tool call on every turn.

C : Add server-side validation that checks whether the order_id exists 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.

Answer: A