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

2. 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.
An engineer submits two requests:
* Request A: "Rename the getUserData function to fetchUserProfile everywhere it's used."
* Request B: "Improve error handling throughout the data processing module-add try/catch blocks, meaningful error messages, and ensure failures don't silently corrupt data." For which request does specifying an explicit multi-phase workflow (such as analyze # propose # implement with review) most improve outcome quality?

A) Neither request benefits significantly
B) Request B, the error handling task
C) Both requests benefit equally
D) Request A, the function rename task

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

4. 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.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?

A) Clear context with /clear , then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
B) Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing,"
"trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
C) Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
D) Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.

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 system has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?

A) Compare accuracy at different confidence thresholds (85%, 90%, 95%) to find the optimal cutoff that maximizes automation while minimizing errors.
B) Run a two-week pilot routing 25% of high-confidence extractions directly to downstream systems and monitor error reports.
C) Analyze accuracy by document type and field to verify high-confidence extractions perform consistently across all segments, not just in aggregate.
D) Verify that 97% accuracy meets requirements for all downstream systems that consume the extracted data.

Solutions: