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Anthropic CCA-F Exam Syllabus Topics:

Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?

A) Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
B) Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.
C) Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.
D) Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.

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

3. Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A) Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
B) Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
C) Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
D) Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.

4. The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?

A) The verification tool is clearing the agent's internal state after each successful validation step.
B) The conversation history isn't being passed in subsequent API requests.
C) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D) The prompt lacks instructions telling Claude to remember information across multiple exchanges.

5. What is the PRIMARY purpose of few-shot prompting?

A) Increase model size.
B) Reduce token costs.
C) Encrypt prompts.
D) Demonstrate desired output patterns.

Solutions: