Anthropic CCAR-F日本語版参考資料、CCAR-F日本語版復習資料

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Workflow design 2. Single-agent and multi-agent architectures 3. Agent orchestration 4. Planning and execution strategies
Topic 2: Context Management & Reliability	15%	- Context handling 1. Context window management 2. Memory strategies 3. Reliability and evaluation 4. Cost and performance optimization
Topic 3: Prompt Engineering & Structured Output	20%	- Prompt design 1. Output validation 2. Few-shot prompting 3. Prompt engineering techniques 4. Structured output and JSON schemas
Topic 4: Tool Design & MCP Integration	18%	- Tool integration 1. Model Context Protocol (MCP) 2. Tool selection and safety 3. Resource and server integration 4. Tool interface design
Topic 5: Claude Code Configuration & Workflows	20%	- Claude Code 1. Configuration and project setup 2. Agent skills 3. Development workflows 4. Code generation and automation

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Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q176-Q181):

質問 # 176
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. Return a success response with a "status" field indicating the error type
C. Throw an exception from the tool handler so the agent framework can catch and log it
D. Log the error server-side and return an empty result to avoid confusing the model

正解：A

解説：
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.

質問 # 177
Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A. Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
B. Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
C. Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
D. Return fields organized into verified and needs_verification objects based on confidence thresholds.

正解：B

解説：
Including a request_review flag based on confidence thresholds, along with reasons for review, gives the agent clear guidance on when human intervention is needed. This reduces both unnecessary reviews and errors caused by low-confidence extractions, improving overall reliability and efficiency.

質問 # 178
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.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A. You check the stop_reason field in each API response-the loop continues while it equals "tool_use" and exits when it changes to "end_turn" or another terminal value.
B. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
C. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
D. You check whether Claude's response contains a text content block-if text is present, the agent has produced its final answer and the loop should exit.

正解：A

質問 # 179
In production, final reports frequently contain claims without proper source attribution. Investigation shows that the web-search and document-analysis agents correctly attach citations to their outputs, but the synthesis agent loses track of which sources support which conclusions when combining findings. What is the most effective architectural change?

A. Retain complete transcripts of every subagent interaction and add a citation-resolution agent that analyzes those logs before report generation.
B. Add a verification step in which the report generator uses semantic-similarity matching against the original sources to reconstruct claim provenance.
C. Require every subagent to return structured claim-to-source mappings that the synthesis agent must preserve and merge when combining findings.
D. Have the coordinator insert source-identifier prefixes into prose before every handoff and parse those prefixes during report generation.

正解：C

解説：
Option C preserves provenance as part of the data contract instead of attempting to reconstruct it after synthesis. Each finding should carry a stable claim identifier and one or more source records containing the URL or document identifier, relevant location, supporting excerpt, retrieval date, and applicable qualification.
The synthesis agent can combine or rewrite claims while retaining their source relationships.
Anthropic's structured-output documentation supports schema-constrained, parseable results for downstream workflows. Its citation documentation explains that reliable citations depend on valid pointers to provided source material. Anthropic's skill guidance similarly recommends cross-referencing each major claim and verifying its citation before completion.
Option A reconstructs attribution probabilistically and may associate a claim with a similar but incorrect passage. Option B encodes metadata inside prose, making transformations and parsing fragile. Option D retains excessive context and adds another model stage to recover information that should never have been discarded. Structured claim-source mappings permit deterministic merging, citation validation, deduplication, and audit trails. The report generator can therefore cite the exact evidence supporting each conclusion without replaying complete research transcripts.

質問 # 180
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.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?

A. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
B. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.
C. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude' s output to schema-compliant JSON.
D. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.

正解：C

解説：
Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular- expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema- constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true. Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool's input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces. Option A remains vulnerable to preambles or malformed output. Option B can select a JSON-looking substring but cannot establish schema validity. Option D adds latency and cost and can still fail repeatedly. The schema-constrained tool contract is consequently the most reliable architecture available in these options.

質問 # 181
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