素敵CCAR-F｜最新のCCAR-F問題無料試験｜試験の準備方法Claude Certified Architect - Foundationsテストサンプル問題

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Reliability	15%	- Context handling 1. Cost and performance optimization 2. Reliability and evaluation 3. Context window management 4. Memory strategies
Tool Design & MCP Integration	18%	- Tool integration 1. Resource and server integration 2. Tool selection and safety 3. Model Context Protocol (MCP) 4. Tool interface design
Claude Code Configuration & Workflows	20%	- Claude Code 1. Configuration and project setup 2. Agent skills 3. Development workflows 4. Code generation and automation
Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Agent orchestration 2. Planning and execution strategies 3. Workflow design 4. Single-agent and multi-agent architectures
Prompt Engineering & Structured Output	20%	- Prompt design 1. Few-shot prompting 2. Output validation 3. Structured output and JSON schemas 4. Prompt engineering techniques

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q32-Q37):

質問 # 32
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 extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?

A. Route extractions for review only when downstream systems report data quality issues or processing failures.
B. Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.
C. Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.
D. Randomly sample 5% of extractions for review.

正解：C

解説：
Limited review capacity should be concentrated on records with the highest probability of semantic error. Schema validation confirms that the response has the correct structure and data types; it does not establish that the extracted values are accurate. Ambiguous wording, contradictory passages, missing evidence, and model-reported uncertainty are direct indicators that an extraction requires human judgment.
Anthropic's reliability guidance recommends permitting Claude to express uncertainty, grounding factual outputs in source material, and validating critical information because hallucination- reduction methods do not eliminate errors completely. These principles support routing uncertain or evidentially conflicted records to reviewers rather than treating syntactically valid output as automatically trustworthy.

質問 # 33
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

A. Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.
B. The coordinator's max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.
C. The AgentDefinitions are configured correctly, but the coordinator's system prompt doesn't explicitly list the available subagent types, preventing the model from knowing they can be invoked.
D. The coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.

正解：D

解説：
Defining specialized subagents is not enough; the coordinator must also be allowed to use the Task tool that actually invokes them. If Task is missing from the coordinator's allowed tools, the model may describe delegation in text but cannot execute the subagent call, so the workflow continues without real delegated results.

質問 # 34
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but 5% of extractions still contain empty arrays or null values for required fields such as citations and methodology.
Spot-checking reveals that the source documents contain this information, but in varied formats-inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?

A. Build a regex-based post-processing layer that scans source documents for citation patterns and methodology keywords, populating empty fields when the model fails to extract them.
B. Add few-shot examples demonstrating extractions from documents with varied structures, showing how to identify citations in different formats and locate methodology details across section types.
C. Modify the schema to make citations and methodology optional, and flag incomplete records for manual review instead of failing validation.
D. Implement retry logic that resends requests when validation detects empty required fields.

正解：B

解説：
Option B targets the remaining failure mode: semantic recognition across heterogeneous document structures.
Strict schemas eliminate malformed JSON and can guarantee that tool inputs conform to declared types, but they cannot force Claude to locate evidence that appears under unfamiliar headings or in atypical sections.
Anthropic's prompting guidance says that a few relevant, diverse, structured examples are among the most reliable ways to improve accuracy and consistency. Examples should therefore show inline citations, reference lists, numbered bibliographies, methodology sections, and methods embedded in introductions, each paired with the correct extracted structure. This teaches the intended evidence-location and granularity rules rather than merely repeating the same request. Option A retries an unchanged prompt and can reproduce the same omission. Option C introduces brittle regex rules that may miss nonstandard citations and mistake keyword mentions for methodology content. Option D suppresses validation failures by weakening the contract, but it does not improve extraction and would convert recoverable omissions into incomplete records.
The examples should be drawn from real failure cases, evaluated on a held-out set, and expanded when monitoring reveals new layouts. Schema constraints and few-shot coverage solve different layers of reliability and should be used together.

質問 # 35
Your conversational assistant frequently generates multiple clarifying questions when users make ambiguous requests. When a user asks "Can you help me with the report?", the assistant responds: "I'd be happy to help! Could you tell me: 1) Which report? 2) What kind of help - drafting, reviewing, or formatting? 3) What's your deadline?" User analytics show a 40% conversation abandonment rate after these multi-question responses.
What's the most effective way to reduce friction while appropriately handling ambiguity?

A. Limit the assistant to one clarifying question per turn, using conversation history to accumulate answers over multiple exchanges rather than requesting everything upfront.
B. Create a lookup table of common request patterns with predefined default interpretations, having the assistant respond with those defaults without stating the assumptions made.
C. Modify the system prompt to instruct the assistant to make reasonable assumptions from available context, state those assumptions explicitly, and offer to adjust if the interpretation is wrong.
D. Add a preprocessing step using a smaller model to classify request ambiguity on a 1-5 scale, routing high-ambiguity requests to a clarification dialog and low-ambiguity requests directly to the assistant.

正解：C

解説：
Making reasonable assumptions based on context and clearly stating them allows the assistant to respond immediately while giving users the opportunity to correct or refine the interpretation, reducing abandonment caused by long multi-question prompts and maintaining engagement.

質問 # 36
After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

A. The subagents need to share a single API connection to enable automatic context sharing between invocations.
B. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
C. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
D. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.

正解：B

解説：
The synthesis agent relies on the coordinator to provide relevant findings from prior subagents. If the coordinator fails to include these outputs in the synthesis prompt, the agent receives no actionable context and cannot produce a meaningful summary or analysis.

質問 # 37
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