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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment |
| Agentic Architecture & Orchestration | 27% | - 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 - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Claude Code Configuration & Workflows | 20% | - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions |
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies |
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Tool distribution and permission controls - Error handling and tool response formatting |
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質問 # 87
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.
After the web-search agent finds 25 sources containing 120,000 tokens of raw content, the document-analysis agent extracts 15,000 tokens of key insights, and the synthesis agent produces a coherent 3,000-token narrative draft, the coordinator must pass context to the report-generation agent for the final output with proper source citations.
What context-passing strategy provides the best balance of completeness and efficiency?
正解：D
解説：
Option A gives the report generator the finished narrative context plus the evidence required to verify and cite its claims. The 3,000-token synthesis draft supplies organization and conclusions, while the source index preserves URLs, supporting excerpts, and claim-to-source mappings without transferring 120,000 tokens of raw search material. Anthropic's effective context-engineering guidance recommends curating the smallest high-signal context and having subagents return condensed, distilled results rather than flooding later stages with their complete working history. Anthropic's citation guidance likewise depends on retaining specific supporting passages and source locations. Option B is complete but inefficient and introduces context pollution that can reduce attention to relevant evidence. Option C separates citation insertion from the reasoning that produced the claims, making incorrect or unsupported matches more likely. Option D retains source names but discards the passages needed to demonstrate that each source actually supports the associated claim. The structured index should use stable source IDs and include only the excerpts necessary for the report's factual claims, with validation that every cited claim maps to at least one evidence entry.
質問 # 88
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.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?
正解：A
解説：
Because the module is unfamiliar, plan mode allows safe, read-only investigation of the stack trace, relevant code, and dependencies before changing production code. Anthropic recommends planning when working with unfamiliar code.
質問 # 89
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 system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field--sometimes returning "cotton blend," other times
"Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?
正解：A
解説：
Option D addresses the observed inconsistency at the model-behavior level. A schema can require that materials be a string, but it cannot teach Claude which lexical form the application considers canonical or demonstrate when a source phrase should populate the field. Anthropic identifies examples as one of the most reliable ways to steer output format, structure, and consistency, recommending several relevant and diverse examples that mirror the real task in its prompting best practices. Complete input-output pairs show both recognition and normalization:
for example, a description containing "60% cotton, 40% polyester" can consistently map to the chosen "cotton/polyester blend" representation. They can also include difficult cases where material information is embedded indirectly in prose. Temperature zero reduces sampling variability but does not repair an underspecified transformation rule. A more capable model likewise lacks the missing formatting convention. Making the field required is dangerous because documents may legitimately omit materials; it can force unsupported values and increase hallucinations. Few-shot examples therefore supply the missing decision boundary while preserving truthful absence handling. The examples should be evaluated on held-out descriptions and supplemented by deterministic post-processing if downstream systems require an exact controlled vocabulary.
質問 # 90
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.
An engineer asks your agent to add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
How should the agent decompose this open-ended task?
正解：B
解説：
The task is open-ended because neither the critical modules nor the required testing sequence is known in advance. The agent should first use lightweight discovery tools to map the repository, locate existing tests, identify central modules, and determine which components have high fan-in, business significance, complex branching, or extensive external dependencies. It can then produce an initial risk-based testing plan and refine it as new dependency information appears.
Anthropic distinguishes predefined workflows from agents that dynamically control their processes and tool usage. Agents are appropriate when the required steps cannot be reliably hardcoded and must adapt to environmental evidence. During execution, they should obtain ground truth through tool results and use that feedback to determine subsequent actions. ( https://www.anthropic.com/research/building-effective-agents ) Anthropic also identifies orchestrator-worker designs as suitable for complex coding and search tasks where the necessary subtasks depend on what the investigation reveals. ( https://www.anthropic.com/research
/building-effective-agents )
Option A assigns effort using directory boundaries rather than risk. Option C exhausts context before delivering value. Option D uses alphabetical order, which has no relationship to impact or coverage priority.
Option B establishes an evidence-driven decomposition: discover, prioritize, test high-impact paths, measure results, and revise the plan as dependencies and uncovered risks emerge.
Official references/topics: Dynamic Task Decomposition; Adaptive Agent Loops; Orchestrator-Workers; Risk-Based Test Planning.
質問 # 91
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.
Your team's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting-sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them.
What is the most effective way to ensure all generated code is consistently formatted?
正解：C
解説：
Formatting is deterministic and should be enforced by deterministic tooling rather than stronger natural- language instructions. A PostToolUse hook executes after a successful tool operation. Matching Edit|Write restricts the hook to file modifications, allowing the edited file path to be passed directly to Prettier.
Anthropic provides this exact pattern in its official hooks guidance: configure a PostToolUse event with an Edit|Write matcher and run Prettier against the modified file. This ensures formatting occurs automatically after every relevant edit instead of depending on Claude remembering to execute a formatter. ( https://code.
claude.com/docs/en/hooks-guide )
Option A still relies on instruction-following and unnecessarily loads procedural material. Option B asks another probabilistic model evaluation to determine whether formatting is correct, even though Prettier can enforce the result directly. A Stop hook also runs later than necessary. Option C improves contextual relevance but does not guarantee compliance; path-scoped rules remain instructions rather than enforcement controls.
The hook should be stored in project settings so the workflow is shared by the team. The formatter's exit status should also be monitored so configuration or syntax failures are visible instead of silently ignored.
Official references/topics: Claude Code Hooks; PostToolUse; Edit|Write Matchers; Deterministic Formatting Enforcement.
質問 # 92
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AnthropicのCCAR-Fの実際のテストのオンラインバージョンを使用すると非常に便利です。 オンライン版の利便性を実感すれば、多くの問題の解決に役立ちます。 Jpshiken教材のCCAR-Fオンライン版の利便性は、主に次の側面に反映されています。一方で、オンライン版は機器に限定されません。 CCAR-Fテスト準備のオンラインバージョンは、電話、コンピューターなどを含むすべての電子機器に適用されます。 一方、CCAR-F学習資料のオンライン版を使用することに決めた場合、WLANネットワークがないことを心配する必要はありません。
CCAR-F認証pdf資料: https://www.jpshiken.com/CCAR-F_shiken.html