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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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 constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
C) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
D) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.

2. 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 show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating.
The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates.
Which approach achieves this guarantee?

A) Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
B) Split the workflow into two sequential agent invocations-a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
C) Add orchestration-layer code that checks the agent's outcome after each loop termination-if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
D) Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.

3. 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 must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?

A) Pre-fill Claude's response with an opening brace to force JSON output, then complete and parse the response.
B) Define a tool with your target schema as input parameters and have Claude call it with the extracted data.
C) Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude' s text response as JSON.
D) Append instructions like "Output only valid JSON matching the schema exactly" and implement retry logic to re-prompt when JSON parsing fails.

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.
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.

5. 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.
You're implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py , error_handlers.py , and audit_logger.py . This is a one-off integration task-these patterns are well-documented in your team wiki and don't need additional project-level documentation.
What's the most effective approach?

A) Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
B) Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.
C) Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
D) Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.

Solutions: