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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.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing.
Which configuration approach correctly applies MCP server scopes?

A) Add the venue server to ~/.claude.json and the playlist server to .mcp.json .
B) Add both servers to the project-level .mcp.json file.
C) Add the venue server to .mcp.json and the playlist server to ~/.claude.json .
D) Add both servers to your local ~/.claude.json .

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.
Your extraction pipeline processes invoices and extracts line items, subtotals, tax amounts, and grand totals.
During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total-sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

A) Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
B) Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.
C) Add a "calculated_total" field where the model sums extracted line items alongside a "stated_total" field. Flag records for human review when values differ.
D) Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.

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.
Your team wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?

A) Add the checklist to the project's CLAUDE.md file under a "Code Review" section.
B) Create a /review slash command containing the checklist, invoked when starting reviews.
C) Configure plan mode as the default for code review sessions.
D) Create a dedicated review subagent with the checklist embedded in its configuration.

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

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 running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A) Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.
B) Update your JSON schema to add a "measurement_type" enum field (precise/informal).
C) Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.
D) Fine-tune the model on the 847 corrected extractions.

Solutions: