Anthropic CCAR-F Prüfungsfragen : Claude Certified Architect - Foundations

During testing, when a customer says, "I need a refund for my recent purchase," the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?

• A. Update the process_refund tool description to state explicitly that order_id must come from a successful lookup_order result and must never be assumed, inferred, or invented.
• B. Preprocess customer messages to extract any mentioned order identifiers and inject them into the conversation before sending the request to Claude.
• C. Change tool_choice from auto to any so Claude must call a tool on every turn.
• D. Add server-side validation that checks whether order_id exists before attempting the refund and returns an error when it does not.

A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?

• A. Specify exact column names.
• B. Increase randomness.
• C. Omit examples.
• D. Remove formatting instructions.

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

Your MCP server implements a check_availability tool that queries an external calendar API.
During testing, you encounter three error conditions: (1) the tool is called with a malformed request missing the required user_email parameter, (2) the calendar API returns a 404 because the specified user doesn't exist in the calendar system, and (3) the calendar API returns a 503 because the service is temporarily unavailable. How should each error be reported according to MCP's error handling design?

• A. Report all three as JSON-RPC protocol errors.
• B. Report all three as tool results with isError: true.
• C. Report error 1 as a JSON-RPC protocol error; report errors 2 and 3 as tool results with isError:
  true.
• D. Report errors 1 and 2 as JSON-RPC protocol errors; report error 3 as a tool result with isError:
  true.

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
In production, you observe that simple fact-checking queries (e.g., "What year was the Paris Climate Agreement signed?") traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query. Complex comparative research benefits from the full pipeline.
Your query distribution is diverse and evolving as users discover new applications. What's the most effective approach to optimize for varying query complexity?

• A. Train a query complexity classifier on labeled historical data to predict optimal subagent combinations, retraining periodically as query patterns evolve.
• B. Implement pattern-based routing that categorizes queries by structure (single-fact vs.
  comparative vs. analytical) and maps each category to a predefined subagent combination.
• C. Create a fast-path for factual questions that bypasses subagents entirely, routing all other queries through the complete pipeline to ensure research thoroughness.
• D. Have the coordinator analyze each query and dynamically decide which subagents to invoke based on its assessment of query requirements.