CCA-F受験対策書、CCA-F再テスト

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows - CI/CD and development integration 1. Plan mode vs direct execution 2. Pipeline automation and review
Topic 2: Agentic Architecture & Orchestration	27%	- Anthropic Agent SDK usage 1. Session state and context handling 2. Task spawning and tool management - Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow
Topic 3: Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting - Structured data generation 1. Output validation and reliability 2. JSON schema enforcement
Topic 4: Context Management & Responsible AI	15%	- Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation - Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies
Topic 5: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration - Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation

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CCA-F再テスト & CCA-F復習対策

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Anthropic Claude Certified Architect Foundations (CCA-F) 認定 CCA-F 試験問題 (Q69-Q74):

質問 # 69
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order, but when attempting to call process_refund, the tool returns a timeout error. The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure. What approach best balances first-contact resolution with appropriate error handling?

A. Escalate immediately to a human agent since the refund action cannot be completed
B. Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically
C. Explain the billing confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later
D. Implement automatic retries with exponential backoff for process_refund, keeping the conversation open until the refund is successfully processed

正解：C

解説：
The agent should use the information it already has to resolve as much of the customer's issue as possible, while being transparent that the refund could not be processed because of a backend timeout. Offering escalation or a later retry preserves trust and supports first-contact resolution without falsely claiming the action was completed.

質問 # 70
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.

正解：B

解説：
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.

質問 # 71
When formatting few-shot examples, what is the most important structural rule to follow to ensure the model produces the desired output?

A. Exclusion: never use XML tags around the examples.
B. Conciseness: output examples should be truncated to save tokens.
C. Randomization: examples should be presented in different formats to increase model adaptability.
D. Format consistency: all examples must follow the identical output structure.

正解：D

解説：
Format consistency is crucial in few-shot prompting. All examples must follow the identical output structure so the model recognizes the strict pattern it needs to emulate in its final generation.

質問 # 72
During an extended codebase exploration session in Claude Code, context degradation begins to occur. What is the most effective strategy to persist key findings across context boundaries?

A. Increase the model's temperature parameter to recall lost context.
B. Have agents maintain scratchpad files to record key findings and reference them in subsequent questions.
C. Use the - -json-schema flag to aggressively compress output.
D. Continuously re-feed the entire codebase into the prompt every 5 turns.

正解：B

解説：
Context degradation in extended sessions causes models to lose track of earlier details. Using scratchpad files to persist critical intermediate state externally ensures that key findings survive context compression and session boundaries.

質問 # 73
In a multi-agent architecture, what is the best practice for handing off context from a coordinator agent to a specialized subagent?

A. Pass only the explicit context, findings, and parameters specifically relevant to the subagent's assigned subtask.
B. Share the entire coordinator conversation history to guarantee maximum context.
C. The subagent automatically inherits the coordinator's context window.
D. Grant the subagent access to a shared global state database where the coordinator writes its findings.

正解：A

解説：
Context isolation is a fundamental principle of multi-agent architecture. Subagents do not automatically inherit parent context. Sharing the full coordinator history wastes tokens and introduces noise. You should explicitly pass only the specific, relevant context needed for the subtask.

質問 # 74
......

試験の概要は毎年新しいポリシーに基づいて変更され、CCA-F質問トレントおよびその他の教育用ソフトウェアは、新しい試験の概要の後、シラバスおよび理論と実践の最新の開発および改訂に従って変更されます対応する変更は、アウトラインに非常に同意します。 CCA-F試験問題は、教材の完全なセットの完璧な形です。教育概要は、カバーされているすべての知識ポイントの概要を網羅し、CCA-F候補者のデッドアングルは、毎年の提案範囲と傾向を示します。

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