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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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.
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (~5% of calls), and business errors ("Order exceeds 30-day return window", "Item already refunded") that are permanent (~12% of calls). Monitoring shows the agent wastes 3-
4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude.
What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A) Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
B) Return structured error responses with "retriable": false for business errors and a customer-friendly explanation for Claude to use.
C) Implement automatic retry logic at the tool layer for technical errors only, passing business errors to Claude without retries.
D) Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.

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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues: (1) compound phrases like "Python and SQL" are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says "Extract all skills mentioned." What's the most effective improvement?

A) Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
B) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
C) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
D) Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."

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.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth-having the agent propose specific code changes for each-before deciding which to implement.
What's the most effective way to structure this exploration?

A) Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
B) Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
C) Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
D) Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.

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.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?

A) Explain the billing, confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later.
B) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically.
C) Implement automatic retries with exponential backoff for process_refund , keeping the conversation open until the refund is successfully processed.
D) Escalate immediately to a human agent since the refund action cannot be completed.

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.
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) Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
B) Modify the refund tool to return an error with message "Amount exceeds policy limit-please escalate" when the threshold is exceeded.
C) Implement a hook to intercept tool calls, when the refund process amount exceeds $500, block it and invoke human escalation.
D) Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."

Solutions: