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

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 2

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 new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py, error_handlers.py, and audit_logger.py. This is a one-off integration task - these patterns are well-documented in your team wiki and don't need additional project-level documentation. What's the most effective approach?

A. Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
B. Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.
C. Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
D. Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.

Question 3

You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.
B. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
C. Create summaries of all source files before exploration begins, loading only these compressed representations into context.
D. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.

Question 4

A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

A. Remove unnecessary sensitive information before sending prompts whenever possible.
B. Store every prompt permanently for auditing.
C. Replace Claude with a smaller local model regardless of task complexity.
D. Send every document without preprocessing to maximize context.

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

Solutions: