Anthropic CCAR-F認定テキスト & CCAR-F再テスト

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures
Topic 2: Context Management & Reliability	15%	- Evaluation and reliability strategies - Production deployment considerations - Managing context windows and information flow
Topic 3: Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability - Designing effective tools for Claude applications
Topic 4: Claude Code Configuration & Workflows	20%	- Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration
Topic 5: Prompt Engineering & Structured Output	20%	- Prompt design strategies - Improving Claude response quality and consistency - Structured output generation and validation

>> Anthropic CCAR-F認定テキスト <<

CCAR-F再テスト、CCAR-Fテストサンプル問題

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q117-Q122):

質問 # 117
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

A. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
C. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
D. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.

正解：D

解説：
The failure originates from an underspecified tool contract. A precise description tells Claude where the required parameter must come from and prevents it from fabricating an identifier.
Server-side validation in D is still valuable as a safeguard, but it detects the error rather than addressing the agent's decision-making. Anthropic identifies detailed tool and parameter descriptions as the most important factor in reliable tool use.

質問 # 118
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?

A. Set tool_choice to {"type": "tool", "name": "extract_metadata"} and process the enrichment requests in subsequent turns after receiving the extracted metadata.
B. Set tool_choice to {"type": "tool", "name": "extract_metadata"} for every API call in the pipeline, ensuring Claude always extracts metadata before any enrichment can occur.
C. Set tool_choice to "auto" and reorder the tool definitions so extract_metadata appears first in the tools array, since Claude prioritizes earlier-listed tools.
D. Set tool_choice to "any" so Claude must use a tool, combined with system prompt instructions prioritizing extract_metadata.

正解：A

解説：
The dependency must be enforced by orchestration rather than left to probabilistic tool selection.
Anthropic documents that tool_choice: {"type": "tool", "name": "..."} forces Claude to invoke the specified tool. By contrast, auto allows Claude to decide whether and which tool to call, while any requires some tool but does not force a particular one.
Option A therefore establishes a deterministic two-stage workflow. The first API turn forces extract_metadata, producing the DOI and other structured paper details. The application validates and stores that result. A subsequent turn then exposes or permits verify_doi and lookup_citations, passing the extracted DOI as explicit state. This design converts an implicit tool dependency into an application-controlled execution graph.

質問 # 119
Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

A. Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
B. Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.
C. Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
D. Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.

正解：A

解説：
A system prompt cannot guarantee compliance, and turn-count hooks may escalate prematurely.
An orchestration-level fallback runs regardless of why the agent loop stopped, ensuring every interaction ends in either resolution or human escalation.

質問 # 120
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent-this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate).
How should you achieve guaranteed compliance?

A. Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
B. Implement a hook to intercept tool calls, when the refund process amount exceeds $500, block it and invoke human escalation.
C. Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
D. Modify the refund tool to return an error with message "Amount exceeds policy limit-please escalate" when the threshold is exceeded.

正解：B

解説：
A mandatory compliance threshold must be enforced outside probabilistic model reasoning. A PreToolUse hook can inspect every attempted process_refund call before execution, compare its amount with the $500 threshold, and deny the call when the limit is exceeded. The host application can then create the human- escalation case using the validated customer and order context.
Anthropic describes hooks as deterministic controls that ensure required actions occur instead of relying on the model to choose them. Agent SDK documentation confirms that a single denying PreToolUse hook blocks the tool call, including MCP tools matched through their qualified tool names. ( https://docs.anthropic.com/en
/docs/claude-code/hooks-guide )
Options A and B improve expected behavior but cannot guarantee compliance. The stated production failure rate demonstrates that prompting alone is insufficient. Option C prevents the refund from completing, but the subsequent escalation still depends on Claude correctly interpreting the returned error and invoking the human tool. It therefore enforces the financial block but not the complete escalation requirement.
Option D enforces both sides of the policy at the orchestration boundary: deny the unauthorized action and route the case to an approved human process. The escalation action should be idempotent and auditable.
Official references/topics: PreToolUse hooks, deterministic policy enforcement, MCP tool interception, human-approval controls.

質問 # 121
Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?

A. Index all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.
B. Have each agent persist a structured export to a known location. On resume, the coordinator loads the manifest and injects relevant state into agent prompts.
C. Persist the coordinator's conversation log containing all task delegations and responses, providing this to agents when resuming.
D. Have each agent maintain its own persistent state file and reload it independently at the start of each session.

正解：B

解説：
Persisting structured exports and a coordinator manifest preserves completed work in a reliable, resumable format. On recovery, the coordinator can reload only the relevant state for each agent, avoiding repeated processing while maintaining fidelity of prior findings without flooding prompts with full conversation logs.

質問 # 122
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