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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
| Claude Code Configuration & Workflows | 20% | - Developer productivity workflows - Integrating Claude Code into development processes - Claude Code usage and configuration |
| Context Management & Reliability | 15% | - Evaluation and reliability strategies - Managing context windows and information flow - Production deployment considerations |
| Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
| Tool Design & MCP Integration | 18% | - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration - Tool safety, reliability, and usability |
市場の他の教育プラットフォームと比較して、ShikenPASSはより信頼性が高く、非常に効率的です。これは、CCAR-F試験に合格したい受験者に高い合格率CCAR-Fの教材を提供し、すべてのお客様が最初の試行でCCAR-F試験に合格しています。ウェブサイトでCCAR-F試験に合格するには、20〜30時間かかります。それは本当に他のことをするために多くの時間とエネルギーを節約するのを助けることができる非常に効率的な試験ツールです。
質問 # 57
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.
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when the pre-specified searches fail, (2) research quality drops for emerging topics that do not match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?
正解：C
解説：
Option A defines what successful research must achieve without hard-coding a brittle search procedure. The subagent receives measurable objectives-breadth, diversity, recency, and relevance-but retains authority to reformulate queries, follow promising leads, and adjust source selection when the topic does not match familiar patterns. Anthropic's account of its multi-agent research system states that subagents need an objective, expected output format, guidance about tools and sources, and clear task boundaries. This is more informative than option B's vague instruction, yet more adaptive than prescribing every query. Option C improves only one known failure path and still constrains the agent to a predefined procedure that may be unsuitable for emerging subjects. Option D adds orchestration complexity and a potentially incorrect classification step without solving over-prescription within either category. The coordinator should describe mandatory constraints separately from strategic guidance: authoritative-source requirements and time boundaries can remain fixed, while query formulation and exploration paths remain agent-controlled.
Evaluation should then measure source coverage, evidence quality, duplication, and discovery of relevant secondary leads rather than whether the subagent followed a particular series of searches.
質問 # 58
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：A
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 # 59
After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?
正解：A
解説：
Option B teaches Claude the actual decision boundaries behind the team's judgments. Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization's deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude's accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.
Option A can suppress legitimate findings merely because they contain a selected keyword. Option C will become lengthy, difficult to maintain, and incapable of enumerating every future code variation. Option D is too vague and may reduce recall by discouraging Claude from reporting uncertain but genuine defects. Few- shot contrasts provide precise, maintainable behavioral guidance without imposing a blanket suppression policy.
質問 # 60
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 raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?
正解：B
解説：
The agent must preserve durable state for several concurrently open issues without keeping every conversational turn in the active context window. Option D creates a structured issue ledger containing identifiers, issue type, requested action, current status, completed steps, unresolved questions, and relevant tool outputs.
Anthropic recommends structured note-taking for long-horizon agents. In this pattern, critical state is written outside the context window and retrieved later, allowing the agent to preserve dependencies and progress across dozens of tool calls. Anthropic also describes compaction as retaining critical details while discarding redundant raw messages and historical tool output. ( https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents ) Option A is better than retaining the entire transcript, but a narrative summary can merge separate issues or obscure exact identifiers and statuses. Option B will discard the refund conversation because it occurred outside the most recent 30 turns. Option C may recover current backend facts, but it cannot reconstruct conversational commitments, prior explanations, or why a particular action remains pending.
A structured context layer should maintain one record per issue and be updated after every material action.
The active prompt can include compact summaries of all open issues and expanded details only for the issue currently being discussed.
Official references/topics: Structured note-taking, external agent memory, context compaction, multi-issue state management.
質問 # 61
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer sees the unfamiliar error message SYNC_CONFLICT: entity version mismatch detected in production logs but does not know which of the 12 services in the codebase generates it. The engineer asks the agent to locate the responsible source code.
What exploration approach will find the responsible code most efficiently?
正解：B
解説：
Option A begins with the strongest available discriminator: the exact production error text. Anthropic's tools reference specifies that Grep searches file contents and returns matching lines, enabling the agent to locate a literal error constant or message without loading the full repository. Searching both the stable code SYNC_CONFLICT and the distinctive phrase protects against cases where the identifier and displayed message are defined separately. The agent can then read only the matching files and follow the surrounding call path, error construction, and service ownership. Option B assumes that errors are stored in conventionally named directories, which may not hold in a twelve-service repository. Option C consumes substantial context before using the most direct evidence. Option D narrows the search to consumers of a known shared error module, but the message may be locally defined, generated from configuration, or wrapped by another component. Grep followed by targeted Read operations follows Anthropic's broader context-engineering principle of retrieving relevant source material just in time instead of placing an entire codebase into the active context.
質問 # 62
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