Anthropic CCA-F - Claude Certified Architect - Foundations Exam
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Total 134 questions
Question #11 (Topic: Exam A)
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps – for instance, noting “the retrieved sources discuss API authentication but lack details on token refresh patterns” – but under the current strict pipeline, this insight isn’t actionable since search has already completed. What’s the most effective architectural change?
A. Add a research planning agent before the search phase that decomposes topics into specific sub-questions.
B. Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
C. Have the coordinator review analysis output for gap indicators and re-invoke search with gap-informed queries when gaps are detected.
D. Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.
Answer: B
Question #12 (Topic: Exam A)
After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?
A. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator
B. Provide the subagent with tool definitions that allow it to request outputs from other subagents via callbacks
C. Pass reference identifiers and configure the subagent with read access to a shared memory store where other subagents deposited their results
D. Include the complete findings from both subagents directly in the synthesis subagent’s prompt
Answer: D
Question #13 (Topic: Exam A)
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?
A. The orchestration layer automatically routes to the next tool based on the order’s status field.
B. The order details are added to the conversation and the model reasons about which action to take.
C. The agent executes the remaining steps in a tool sequence planned at the start of the request.
D. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
Answer: B
Question #14 (Topic: Exam A)
After investigating a billing dispute over 25+ turns, you’ve identified that duplicate charges occurred due to a payment gateway timeout triggering retry logic. The required refund ($847) exceeds your $500 authorization limit You need to call escalate_to_human, and the human agent won’t have access to your conversation transcript. What context should you pass to enable effective resolution?
A. A structured summary: customer ID, root cause, refund amount, and recommended action.
B. Your diagnosis and the refund amount only.
C. The customer’s original complaint verbatim plus the tool result excerpts showing duplicate transactions.
D. The complete conversation transcript with all tool results.
Answer: A
Question #15 (Topic: Exam A)
Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval – beyond the agent’s authorization level. How should the workflow handle this mid-process escalation?
A. Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human.
B. Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
C. Call escalate_to_human passing only the customer’s original message.
D. Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
Answer: A
