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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A) Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.
B) Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.
C) Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
D) Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.

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.
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 missing required 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) Make two separate API calls-first extracting information as text, then asking Claude to format that text as JSON.
B) Parse Claude's text response with regex patterns to extract JSON objects, using retry logic for malformed responses.
C) Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.
D) Define a tool with an input schema matching your required JSON structure and extract the data from Claude's tool_use response.

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.
A security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A) Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.
B) Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.
C) Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.
D) Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.

4. 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 system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?

A) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.
B) Add prompt instructions to return null for any field where information is not directly stated in the source.
C) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.
D) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.

5. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through 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.
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true,
"content": [{"type": "text", "text": "Operation failed"}]} . The agent cannot distinguish between error types.
What's the most effective improvement?

A) Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
B) Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
C) Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
D) Enhance error responses with structured metadata-include error_category (transient/validation
/permission), isRetryable boolean, and a description of what caused the failure.

Solutions: