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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions |
| Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - Tool schema design and interface boundaries |
| Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies |
| 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 |
確かにAnthropic CCAR-F試験に準備する過程は苦しいんですけど、Anthropic CCAR-F資格認定を手に入れるなり、IT業界で仕事のより広い将来性を持っています。あなたの努力を無駄にするのは我々CertJukenのすべきことです。CertJukenのレビューから見ると、弊社CertJukenは提供している質高い試験資料は大勢の顧客様の認可を受け取ったと考えられます。我々はあなたにAnthropic CCAR-F試験に合格させるために、全力を尽くします。
質問 # 57
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You're implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py , error_handlers.py , and audit_logger.py . This is a one-off integration task-these patterns are well-documented in your team wiki and don't need additional project-level documentation.
What's the most effective approach?
正解：D
解説：
Direct @ references provide Claude with the exact implementations it must imitate. Anthropic documents that referencing a file with @ includes the full file content in the conversation, and multiple files can be referenced in one message. This gives Claude immediate access to the real transaction boundaries, exception structures, audit fields, naming conventions, and helper APIs used by the project. ( https://code.claude.com/docs/en
/common-workflows )
Option B is inappropriate because the task is explicitly one-off and the conventions are already documented elsewhere. CLAUDE.md is loaded into every session and should contain concise information that broadly applies to the project. Adding detailed implementation material for a single integration would consume context unnecessarily. Anthropic recommends moving occasional procedures to skills and keeping CLAUDE.
md limited to persistent, widely applicable guidance. ( https://code.claude.com/docs/en/memory ) Option C loses precision because a natural-language summary may omit subtle but important code behavior.
Option D asks Claude to rediscover files that have already been identified, increasing exploration time and context usage.
The most effective prompt should reference all three modules, identify which pattern each demonstrates, specify the new module's required behavior, and request focused tests proving that the established conventions were followed.
Official references/topics: @ file references, rich prompt context, CLAUDE.md scope, pattern-based implementation.
質問 # 58
During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops-Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?
正解：A
解説：
Option A is the only configuration that establishes both required limits inside Claude Code. The official CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode; Claude Code exits with an error when that limit is reached. It defines --max-budget-usd as the maximum dollar expenditure on API calls before execution stops, including applicable subagent expenditure.
These controls address different failure dimensions. The turn limit prevents an investigation from continuing through excessive read-search-analyze cycles, while the budget limit stops an invocation whose expensive turns consume the monetary allowance before reaching the turn ceiling. Supplying both therefore creates an effective per-run boundary.
Option B controls how permission requests are handled; it does not limit the number of already permitted tool calls or API expenditure. Option C applies an external wall-clock timeout and merely observes cost, violating the requirement that Claude Code itself enforce both limits. Option D lowers expected cost per turn but creates no hard ceiling: the agent can still perform many iterations and exceed the intended budget. A cheaper model also does not guarantee that the review will terminate within a predictable number of turns.
質問 # 59
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated review generates many findings per pull request, but developer feedback shows that approximately half are dismissed as "not worth addressing." Analysis reveals that these findings are often technically accurate but involve minor style preferences or patterns that are acceptable in the project.
Before adding infrastructure complexity, what prompt-design change would most effectively reduce dismissals while maintaining detection of genuine issues?
正解：A
解説：
Option C defines the reviewer's actual objective instead of relying on an undefined notion of confidence. The prompt should identify reportable categories such as correctness defects, security vulnerabilities, data-loss risks, broken requirements, and meaningful performance regressions. It should also specify that cosmetic preferences, lint-enforced formatting, speculative refactoring, and intentional repository conventions must not be reported unless they cause concrete incorrect behavior.
Anthropic's Claude Code best-practices guidance recommends telling a reviewer to report gaps affecting correctness or stated requirements and to treat other observations as optional. Without such criteria, a reviewer asked to "find issues" will predictably return technically defensible but operationally low-value observations.
質問 # 60
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
During initial testing of the automated review pipeline, you notice that reviews on large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8?12 per run because of extensive agentic loops. Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort once it reaches both a fixed iteration count and a fixed dollar amount, enforced by Claude Code itself rather than by the surrounding job runner.
Which configuration change directly enforces both per-invocation caps?
正解：B
解説：
Option C applies the two native stopping controls required by the question. Anthropic's Claude Code CLI reference defines --max-turns as the maximum number of agentic turns permitted in print mode and --max-budget-usd as the maximum API expenditure for that invocation. When the turn limit is reached, the run exits with an error. Spending by subagents counts toward the budget cap, and current Claude Code versions stop remaining background subagents when the limit is reached. These flags therefore control the complete invocation rather than merely one model request.
質問 # 61
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?
正解：D
解説：
Option A exposes both independent invocations in the same assistant turn, allowing the Agent SDK or application tool runner to execute them concurrently. The coordinator can then receive both results together and continue with synthesis only after the independent research branches have completed.
Anthropic's parallel tool-use documentation explains that a response may contain multiple tool- use blocks. Independent, read-only operations can be executed concurrently to reduce latency, after which all corresponding tool results should be returned together. The term "Agent" is used here because current Claude Agent SDK releases renamed the earlier "Task" tool.
質問 # 62
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