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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Context Management & Reliability | 15% | - Context handling
|
CCAR-F勉強のトレントを購入すると、24時間オンラインの効率的なサービスを提供します。 CCAR-F学習資料に関するご質問はいつでもお問い合わせいただけます。また、いつでもご連絡いただけます。もちろん、忙しくてオンラインで連絡する時間がない場合は、心配しないで、いつでもCCAR-Fガイド資料に関する問題をメールでお知らせください。カスタマーサービスからすぐにメールが届きます。一言で言えば、24時間オンラインの効率的なサービスは、すべての問題を解決して試験に合格するのに役立つと考えています。
質問 # 170
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?
正解：B
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. ( https://docs.anthropic.
com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Option B may waste reviewer capacity on clear, well-supported values while overlooking ambiguous errors in other fields. Option C is reactive: downstream acceptance does not guarantee semantic correctness, and silent errors may never produce processing failures. Option D provides an unbiased estimate of overall quality and should be retained as a secondary audit mechanism, but random selection is not the most efficient way to intercept risky records when only 5% can be reviewed.
In production, model confidence should not be treated as a calibrated probability by itself. It should be combined with objective signals such as contradictory source spans, missing citations, OCR quality, validation warnings, document type, and business impact.
Official references/topics: Human-in-the-Loop Review, Uncertainty Handling, Source Grounding, Risk- Based Escalation.
質問 # 171
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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers.
What task decomposition approach would be most effective?
正解：C
解説：
The investigation path cannot be reliably predetermined because the responsible files, components, and execution sequence are unknown. The agent should begin with available evidence-such as route definitions, stack traces, logs, or endpoint references-and use each discovery to decide the next search, file read, or diagnostic action.
Anthropic distinguishes predefined workflows from agents that dynamically direct their own processes and tool usage. Agents are appropriate for open-ended problems where the required number and nature of the steps cannot be predicted or encoded as a fixed path. During execution, the agent should obtain ground truth from tool results and adapt its plan based on that environmental feedback. ( https://www.anthropic.com
/engineering/building-effective-agents )
Option A requires a comprehensive plan before the agent has inspected the code, so the plan would rest on unsupported assumptions. Option B forces every investigation through the same sequence even when an early discovery makes later steps irrelevant or identifies a different dependency path. Option C assumes the four layers can be investigated independently; tracing an intermittent request failure usually involves dependencies revealed sequentially across layers.
Option D implements an adaptive agent loop: inspect, form a hypothesis, use tools, evaluate the evidence, and generate the next subtask. The workflow should still include stopping conditions, testable hypotheses, and escalation when evidence remains inconclusive.
Official references/topics: Adaptive Agent Loops, Dynamic Task Decomposition, Tool Feedback, Open- Ended Coding Investigations.
質問 # 172
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.
1.5An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~6,000 lines total).
What's the most effective next step for building understanding while managing context constraints?
正解：C
解説：
The correct objective is to construct an architectural map before consuming the full implementation.
Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger.
This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. (
https://code.claude.com/docs/en/best-practices )
Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design.
Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it.
Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation.
質問 # 173
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.
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like
'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order , which catches all exceptions and returns a tool_result with is_error: true and the message
"Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools.
Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?
正解：C
解説：
Option B preserves the formal failure indicator while making the returned content operationally useful.
Anthropic's tool-use guidance states that when a tool fails, the application should return the error through tool_result content and mark it with is_error: true . Claude can then use that information to retry with corrected input, seek clarification, or explain the limitation. ( https://docs.anthropic.com/en/docs/agents-and- tools/tool-use/bash-tool ) The two example messages identify both the cause and the appropriate recovery path. "Order not found" indicates that repeating the same identifier will not help and proposes an alternative lookup method.
"Database timeout" identifies a transient infrastructure condition for which a retry is reasonable. This eliminates blind repetition without removing Claude's ability to adapt.
Option A can be useful for tightly bounded low-level retries, but it does not solve permanent errors or tell the agent what happened after retries fail. Option C incorrectly disguises a failed operation as successful tool content. Option D moves semantic knowledge into hardcoded orchestration logic, making the system more rigid and duplicating information already known by the tool.
The most reliable tool interface combines the error flag, a stable error type, a retryability indicator, a concise causal explanation, and a recommended recovery action.
Official references/topics: is_error handling, instructive tool results, recovery guidance, resilient MCP tool design.
質問 # 174
After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?
正解：B
解説：
Progressively summarizing older conversation turns condenses historical context while keeping recent interactions verbatim, reducing token load and maintaining coherence. This approach balances response speed, memory efficiency, and user experience by preserving essential context without requiring users to repeatedly provide information.
質問 # 175
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