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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Responsible AI	15%	- Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization - Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles
Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems - Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. Tools, resources, and prompts integration 2. MCP server and client setup - Tool definition and best practices 1. Error handling and validation 2. Tool descriptions and selection logic
Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. Few-shot and chain-of-thought prompting 2. System prompts and role framing - Structured data generation 1. Output validation and reliability 2. JSON schema enforcement
Claude Code Configuration & Workflows	20%	- CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution - Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows

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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q49-Q54):

NEW QUESTION # 49
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. 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.
C. 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.
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.

Answer: A

Explanation:
Enforcing the approval threshold within the tool itself makes it tamper-proof and independent of agent behavior or prompts. The tool controls disbursement and ensures manager approval is required for amounts over $500, preventing accidental or intentional bypass.

NEW QUESTION # 50
When using Claude Code for test generation and feature implementation, what is the recommended Test-Driven Development (TDD) iterative refinement pattern?

A. Run the /test slash command to automatically infer and generate missing unit tests for the entire project.
B. Write implementation write tests -> prompt Claude to 'make it better' -> refactor.
C. Use Plan Mode to write all tests simultaneously, then execute them in a single batch.
D. Write a failing test -> implement code to pass the test -> run tests -> refine implementation while keeping tests green.

Answer: D

NEW QUESTION # 51
You are assigned to restructure a monolithic application into microservices, requiring architectural decisions and changes across 45+ files. Which execution approach in Claude Code is recommended?

A. Start with direct execution and make changes incrementally, letting the implementation reveal the natural boundaries.
B. Write a static Prompt Chain that forces the agent to read and refactor each file sequentially.
C. Enter plan mode to explore the codebase and design an implementation strategy before executing changes.
D. Use direct execution with a massive upfront prompt detailing how every service should be structured.

Answer: C

Explanation:
Plan mode is specifically designed for complex, multi-file tasks involving architectural decisions. It enables safe codebase exploration and design before committing to file modifications, preventing costly rework. Direct execution is intended for simple, well-scoped tasks.

NEW QUESTION # 52
When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

A. Return the error message in the tool result content with the isError flag set to true
B. Throw an exception from the tool handler so the agent framework can catch and log it
C. Log the error server-side and return an empty result to avoid confusing the model
D. Return a success response with a "status" field indicating the error type

Answer: A

Explanation:
Returning structured tool results with isError set to true allows the agent to receive explicit error signals while preserving the error message. This enables the agent to reason about the failure, choose appropriate recovery actions, and provide meaningful feedback to the user.

NEW QUESTION # 53
In a coordinator-subagent research system, a web search subagent times out while attempting to retrieve information on a complex topic. How should this failure be propagated back to the coordinator agent?

A. The subagent should catch the timeout and return an empty JSON array L] marked as successful so the workflow continues smoothly.
B. Return structured error context to the coordinator, including the failure type, the attempted query, any partial results, and potential alternative approaches.
C. Implement automatic retry logic indefinitely within the subagent, returning a generic search unavailable status if it ultimately fails.
D. Propagate the timeout exception directly to a top-level handler to immediately terminate the entire research workflow.

Answer: B

Explanation:
Structured error propagation is critical. Returning structured error context (failure type, what was attempted, partial results) enables the coordinator to make an intelligent recovery decision. Silently suppressing the error by returning an empty result is a severe anti-pattern, and returning generic messages hides context needed for recovery.

NEW QUESTION # 54
......

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