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Anthropic CCA-F Exam Syllabus Topics:

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

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

2. Your agent uses three tools: get_property_details(property_id) returns data including street address, get_price_history(property_id) returns historical pricing, and get_neighborhood_info(address) returns area statistics. You observe that get_neighborhood_info always requires get_property_details first just to extract the address, even when users specify the property by ID. This creates unnecessary latency and failure coupling - if the first call fails, the neighborhood request also fails. What tool design change best addresses this?

A) Change get_neighborhood_info to accept property_id, resolving the address internally.
B) Consolidate into a single get_property_with_neighborhood(property_id) tool returning both datasets.
C) Create a lookup_address(property_id) helper tool for retrieving addresses.
D) Add retry logic and timeout handling to get_property_details.

3. 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) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
C) The prompt lacks instructions telling Claude to remember information across multiple exchanges.
D) The conversation history isn't being passed in subsequent API requests.

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

5. Which factor MOST directly affects API cost?

A) Screen resolution
B) Internet speed
C) Number of processed tokens
D) CPU clock frequency

Solutions: