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Anthropic CCA-F Exam Syllabus Topics:

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

1. Your expense reimbursement agent processes employee requests using a
process_reimbursement tool. Company policy requires that reimbursements above $500 must be approved by a manager before funds are disbursed. The agent handles hundreds of requests daily, and you need the threshold enforcement to be tamper-proof regardless of how the agent is prompted. Which design ensures the $500 approval threshold cannot be bypassed?

A) The process_reimbursement tool accepts amount and details, and internally enforces the threshold: amounts <$500 are auto-disbursed and the tool returns a success confirmation; amounts >$500 cause the tool to create a pending approval request and return a status indicating manager review is pending.
B) Implement the threshold check in a PreToolUse hook that inspects the amount parameter before process_reimbursement executes. If the amount exceeds $500, the hook modifies the tool call to add a requires_approval: true flag, which the tool checks before disbursing.
C) Provide two tools: auto_reimburse (hard-coded limit of $500) and request_manager_approval.
Include detailed system prompt instructions telling the agent to check the amount and call the appropriate tool. Add a PostToolUse hook that logs which tool was called for auditing.
D) The process_reimbursement tool accepts an approved_by_manager: boolean parameter. The system prompt instructs the agent to only set this to true after confirming that a manager has approved the request. A nightly audit script reviews all reimbursements where approved_by_manager was set to true.

2. A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate their account. What should the agent do?

A) Immediately call escalate_to_human with the conversation history.
B) First call get_customer and lookup_order to gather account context, then escalate to a human agent.
C) Acknowledge the frustration and ask one targeted question to understand the specific issue before escalating.
D) Briefly explain what the agent can help with and offer to resolve the issue quickly, escalating only if the customer repeats their request.

3. A company is building its first production Claude application. Which principle should guide the initial deployment?

A) Start with a simple design and iteratively evaluate improvements.
B) Disable monitoring to reduce costs.
C) Optimize only for response speed.
D) Maximize prompt complexity immediately.

4. Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A) Return fields organized into verified and needs_verification objects based on confidence thresholds.
B) Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
C) Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
D) Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.

5. A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?

A) Remove role instructions.
B) Add reliable retrieval sources.
C) Raise temperature.
D) Shorten all outputs.

Solutions: