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Anthropic CCAR-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
| Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Production deployment considerations - Evaluation and reliability strategies |
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
Anthropic Claude Certified Architect - Foundations Sample Questions:
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?
- A. Modify the refund tool to return an error with message "Amount exceeds policy limit - please escalate" when threshold is exceeded.
- B. Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
- C. Implement a hook to intercept tool calls; when the refund process amount exceeds $500, block it and invoke human escalation.
- D. Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
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The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?
- A. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
- B. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes
- C. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
- D. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
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Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?
- A. Add a confirmation step that requires users to type "CONFIRM DELETE" before delete_file executes.
- B. Expand tool descriptions to clarify use cases, adding guidance like "Do not use for backup files" to delete_file.
- C. Add few-shot examples to the system prompt demonstrating that requests involving "backup" or
"old" should use archive_file. - D. Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.
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Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like 'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order, which catches all exceptions and returns a tool_result with is_error:
true and the message "Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools. Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?
- A. Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.
- B. Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, tags each as "retry," "try_alternative," or "escalate," and appends that recommendation to the tool result.
- C. Return error-type-specific messages with is_error: true, e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.
- D. Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
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Which factor MOST directly affects API cost?
- A. Internet speed
- B. CPU clock frequency
- C. Screen resolution
- D. Number of processed tokens
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