Anthropic Claude Certified Architect - Foundations : CCAR-F

The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate--it generates messages such as, "I'll ask the web-search agent to find sources on this topic"--but no subagent execution occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

• A. The coordinator's max_tokens setting is too low, causing the subagent invocation to be truncated before the agent-type parameter is specified.
• B. The coordinator's allowedTools configuration does not include "Agent"--called "Task" in older SDK releases--so it cannot invoke the tool required to spawn subagents.
• C. Subagent context isolation prevents task descriptions from reaching subagents unless explicit context forwarding is configured in ClaudeAgentOptions.
• D. The AgentDefinition objects are configured correctly, but the coordinator's system prompt does not explicitly list the available subagent types.

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 asks the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain- specific use (e.g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?

• A. Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.
• B. Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.
• C. Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
• D. Use Grep to search for the function's original name across the codebase.

You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category--such as security or style--and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?

• A. Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.
• B. Remove the issue_category field and track dismissal rates only at the individual-finding level.
• C. Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.
• D. Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.

A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

• A. First call get_customer and lookup_order to gather account context, then escalate to a human agent.
• B. Immediately call escalate_to_human with the conversation history.
• C. Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
• D. Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.

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