Claude Certified Architect Foundations CCAR-F Exam Questions

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Question #21 (Topic: Demo Questions)

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.

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?

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.

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.

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.

Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, then routes to hardcoded retry or escalation logic.

Correct Answer: B

Explanation:

Option B preserves the formal failure indicator while making the returned content operationally useful. Anthropic’s tool-use guidance states that when a tool fails, the application should return the error through tool_result content and mark it with is_error: true . Claude can then use that information to retry with corrected input, seek clarification, or explain the limitation. ( https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/bash-tool )

The two example messages identify both the cause and the appropriate recovery path. “Order not found” indicates that repeating the same identifier will not help and proposes an alternative lookup method. “Database timeout” identifies a transient infrastructure condition for which a retry is reasonable. This eliminates blind repetition without removing Claude’s ability to adapt.

Option A can be useful for tightly bounded low-level retries, but it does not solve permanent errors or tell the agent what happened after retries fail. Option C incorrectly disguises a failed operation as successful tool content. Option D moves semantic knowledge into hardcoded orchestration logic, making the system more rigid and duplicating information already known by the tool.

The most reliable tool interface combines the error flag, a stable error type, a retryability indicator, a concise causal explanation, and a recommended recovery action.

Official references/topics: is_error handling, instructive tool results, recovery guidance, resilient MCP tool design.

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