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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
|
| Topic 2: Prompt Engineering & Structured Output | 20% | - Structured data generation
|
| Topic 3: Context Management & Responsible AI | 15% | - Context window optimization
|
| Topic 4: Claude Code Configuration & Workflows | 20% | - Configuration files and structure
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Anthropic Agent SDK usage
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
1. Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?
A) Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
B) Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
C) Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.
D) Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".
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) 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.
B) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
C) 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.
D) 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.
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.
Your team wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session. Which approach best addresses this workflow need?
A) Create a dedicated review subagent with the checklist embedded in its configuration.
B) Create a /review slash command containing the checklist, invoked when starting reviews.
C) Configure plan mode as the default for code review sessions.
D) Add the checklist to the project's CLAUDE.md file under a "Code Review" section.
4. Which practice MOST improves prompt maintainability?
A) Clearly separated sections with headings.
B) One paragraph containing all instructions.
C) Minimal punctuation.
D) Random ordering.
5. Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?
A) Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.
B) Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.
C) Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
D) Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |








