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Anthropic CCA-F Exam Syllabus Topics:

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

Question 1

The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

A. Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
B. The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.
C. The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.
D. The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.

Question 2

You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

A. Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
B. Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
C. Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
D. Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.

Question 3

Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

A. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues
B. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
C. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
D. Proceed with additional lookups without modifying the existing tool output context

Question 4

Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (5% of calls), and business errors ("Order exceeds
30 day return window", "Item already refunded") that are permanent (12% of calls). Monitoring shows the agent wastes 3-4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude. What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A. Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
B. Return structured error responses with retriable: false for business errors and a customer-friendly explanation for Claude to use.
C. Implement automatic retry logic at the tool level for technical errors only, passing business errors to Claude without retries.
D. Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.

Question 5

A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

A. Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
B. Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
C. Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.
D. Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.

Solutions: