Anthropic CCA-F Exam Syllabus Topics:

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

Question #1
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. Proceed with additional lookups without modifying the existing tool output context
C. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
D. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions

Question #2
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 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.
B. 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.
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. 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.

Question #3
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires. What's the most effective way to start your iterative workflow?

A. Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
B. Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
C. Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
D. Use plan mode to analyze the current/products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

Question #4
An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?

A. API key description
B. System prompt
C. Retrieved documents
D. User prompt only

Question #5
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.

Solutions: