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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

Your post_content tool requires user confirmation before publishing. The current workflow displays "Ready to post to social media. Confirm?" and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors - all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?

A. Require users to type a confirmation phrase instead of clicking a button
B. Auto-approve routine posts and only require explicit confirmation for unusual patterns like posting to new accounts or large audiences
C. Add a mandatory waiting period before the confirm option becomes available
D. Include the complete post text, target account, scheduled time, and platform in the confirmation request

Question 2

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. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.
C. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
D. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.

Question 3

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A. Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
B. Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
C. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
D. Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.

Question 4

During testing, when a customer says, "I need a refund for my recent purchase," the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?

A. Preprocess customer messages to extract any mentioned order identifiers and inject them into the conversation before sending the request to Claude.
B. Update the process_refund tool description to state explicitly that order_id must come from a successful lookup_order result and must never be assumed, inferred, or invented.
C. Change tool_choice from auto to any so Claude must call a tool on every turn.
D. Add server-side validation that checks whether order_id exists before attempting the refund and returns an error when it does not.

Question 5

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 invoice extraction uses tool use with strict JSON schemas. JSON syntax errors never occur, but 12% of extractions fail semantic validation--for example, line-item amounts do not sum to the extracted total, or vendor IDs do not match valid formats. These failures currently route to manual review.
What is the most effective approach to reduce manual-review volume while maintaining accuracy?

A. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.
B. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.
C. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.
D. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.

Solutions: