Anthropic CCAR-F : Claude Certified Architect - Foundations

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A. The conversation history isn't being passed in subsequent API requests.
B. The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C. The verification tool is clearing the agent's internal state after each successful validation step.
D. Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.

Question 2

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 extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
B. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
C. Redesign the schema so amended fields capture multiple values, each with source location and effective date.
D. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.

Question 3

When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

A. Return a success response with a "status" field indicating the error type
B. Throw an exception from the tool handler so the agent framework can catch and log it
C. Log the error server-side and return an empty result to avoid confusing the model
D. Return the error message in the tool result content with the isError flag set to true

Question 4

A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

A. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.
B. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
C. First call get_customer and lookup_order to gather account context, then escalate to a human agent.
D. Immediately call escalate_to_human with the conversation history.

Question 5

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 extraction pipeline processes restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-- prices as "$12" vs "12.00", dietary info as icons vs text.
What's the most reliable approach?

A. Use separate extraction calls for each field to ensure consistent handling of each type.
B. Define a strict output schema and include format normalization rules in your prompt.
C. Request multiple extraction attempts per document and select the most common format.
D. Extract data as-is and normalize formats in post-processing code after Claude returns.

Solutions: