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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

A. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
B. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
C. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.
D. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.

Question 2

Which practice MOST improves prompt maintainability?

A. Clearly separated sections with headings.
B. One paragraph containing all instructions.
C. Minimal punctuation.
D. Random ordering.

Question 3

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 validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?

A. Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.
B. Randomly sample 5% of extractions for review.
C. Route extractions for review only when downstream systems report data quality issues or processing failures.
D. Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.

Question 4

A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?

A. Specify exact column names.
B. Omit examples.
C. Increase randomness.
D. Remove formatting instructions.

Question 5

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

Solutions: