Anthropic CCAR-F出題内容、CCAR-F試験問題

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Reliability	15%	- Context window optimization and prioritization - Token budget management and cost control - Idempotency, consistency and failure resilience - Context pruning and summarization strategies
Tool Design & MCP Integration	18%	- Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Tool distribution and permission controls
Agentic Architecture & Orchestration	27%	- Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection
Claude Code Configuration & Workflows	20%	- CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution
Prompt Engineering & Structured Output	20%	- Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment

>> Anthropic CCAR-F出題内容 <<

Anthropic CCAR-F試験問題 & CCAR-F関連日本語版問題集

状況によってはあなたを助けたり破ったりすることができるこの運命的な試験について、当社はこれらのCCAR-F練習資料を説明責任を持って作成しました。他の場所に受け入れられる可能性が高くなり、より高い給料や受け入れが得られることを理解しています。 CCAR-Fトレーニング資料は当社の責任会社によって作成されているため、他の多くのメリットも得られます。参考のために無料のデモを提供し、専門家が自由に作成できる場合は新しいアップデートをお送りします。

Anthropic Claude Certified Architect - Foundations 認定 CCAR-F 試験問題 (Q190-Q195):

質問 # 190
After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?

A. Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.
B. Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
C. Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
D. Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.

正解：C

解説：
Claude selects tools primarily from their names and descriptions. A specific description distinguishing dependency-graph analysis-including direct imports, transitive dependencies, and cycles-from simple text search makes analyze_dependencies the clearer choice.

質問 # 191
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 system has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A. Add few-shot examples to your prompt demonstrating correct handling of informal measurements--extracting them verbatim rather than converting or omitting them.
B. Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.
C. Fine-tune the model on the 847 corrected extractions.
D. Update your JSON schema to add a "measurement_type" enum field (precise/informal).

正解：A

解説：
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful, splash, and pinch exactly.
Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities.

質問 # 192
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today.
The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge. What approach best balances efficiency and accuracy?

A. Resume the session without informing the agent about the changed files
B. Resume the session and inform the agent which specific files changed for targeted re-analysis
C. Start fresh session to ensure the agent works with current codebase state without stale assumptions
D. Resume the session and immediately have the agent re-read all 12 previously analyzed files

正解：B

解説：
Resuming preserves the hour of accumulated architectural context, while re-reading the three modified files refreshes stale information without redundantly analyzing all 12 files. This balances efficiency with codebase accuracy.

質問 # 193
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?

A. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.
B. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.
C. Structure the coordinator to emit both Agent tool calls--formerly Task tool calls--in a single response rather than across separate conversation turns.
D. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.

正解：C

解説：
Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent-spawning capability the Agent tool; Task is its former name. Anthropic's SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks, enabling independent calls to be handled as one parallel group rather than as serial model turns.

質問 # 194
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average
18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?

A. Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and impose stricter evidence requirements on rendering-related issues.
B. Configure separate GitHub Actions workflow files for each code area: one for generated code with findings suppressed, one for rendering code with custom instructions, and one general workflow for everything else.
C. Add detailed explanations to the project's CLAUDE.md describing intentional patterns, stating that CI handles linting, and identifying src/gen/ as automatically generated code.
D. Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.

正解：D

解説：
Option A uses the dedicated configuration mechanism for Anthropic's managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.
The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.
Options B and C configure self-hosted GitHub Actions workflows, but the scenario concerns managed Code Review running through the Claude GitHub App on Anthropic's infrastructure. Those workflow prompts do not govern the managed reviewer. Option D supplies useful general project context, but CLAUDE.md applies to all Claude Code tasks and has lower review-specific authority. The documentation states that REVIEW.md is the stronger control for changing what managed Code Review flags and how findings are reported. It therefore provides the most targeted solution with the least unnecessary infrastructure.

質問 # 195
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CCAR-F学習教材を世界中に確実に紹介し、幸運とより良い機会を求めるすべての人々が自分の人生の価値を実現できるようにするという大胆な考えを持っています。したがって、CCAR-F練習問題は、CCAR-F試験に合格し、より良い未来を勝ち取るのに役立ちます。また、常に先駆的な精神を持ち続け、あなたの道を歩むプロジェクトに積極的に取り組みます。 CCAR-Fトレーニング資料は、その素晴らしい品質のためにあなたを決して失望させません。

CCAR-F試験問題: https://www.jptestking.com/CCAR-F-exam.html