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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution |
| Topic 2: Integration | 19% | - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools |
| Topic 3: Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment |
| Topic 4: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques |
| Topic 5: Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions |
| Topic 6: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines - Define evaluation metrics and success criteria |
| Topic 7: Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation |
CCAR-P試験の質問は、Pass4Testお客様のニーズを最大限に満たすことができます。また、CCAR-P学習教材は、お客様の観点から最大限に設計されています。 したがって、運用の複雑さを心配する必要はありません。 システムの学習インターフェイスに入り、WindowsソフトウェアでCCAR-P学習教材の練習を開始すると、インターフェイスに小さなボタンが表示されます。 これらのボタンには回答が表示され、学習プロセスを妨げないように、CCAR-P試験クイズのClaude Certified Architect - Professional学習中に回答を非表示にすることができます。 すべての面が完璧です。
質問 # 121
You are supporting a team whose Claude Code sessions consistently load 60 or more MCP tools from many servers, exhausting context budget before the session begins.
Which adjustment most directly addresses this issue without removing capability?
正解：D
解説：
Tool Search applies progressive discovery to a large tool catalog. Instead of inserting every MCP tool's complete JSON schema and description into the initial context, Claude receives limited discovery information and loads the full definitions only when a task requires them. This preserves the available context for instructions, repository content, conversation history, and tool results without removing the underlying capabilities.
Anthropic's Claude Code documentation states that Tool Search keeps MCP context usage low by deferring tool definitions until Claude needs them. Only the tools actually selected enter the working context. Claude Code MCP Tool Search Anthropic's advanced-tool-use guidance also describes conventional multi-server configurations consuming tens of thousands of tokens before work begins, while on-demand discovery substantially reduces that overhead. Advanced Tool Use Option B solves context pressure by destroying required functionality. Option C worsens the constraint by adding more prompt tokens. Option D expands the exact source of context bloat.
The team should also improve server and tool descriptions so discovery returns the correct tools, monitor tool- selection quality, and keep frequently required foundational tools non-deferred only when evaluations justify the additional context cost.
Study Guide references/topics: MCP scaling; Tool Search; progressive discovery; deferred schemas; context- budget management; tool-selection accuracy.
質問 # 122
A Claude architect is leading the discovery phase for a new AI-powered customer service solution.
Which two activities are characteristic of structured discovery and requirement gathering for a Claude-based deployment? (Select two.)
正解：B、E
解説：
Structured discovery begins by eliciting constraints from the people who own the business outcome, operate the process, manage risk, and will use or support the deployed system. Stakeholder workshops in Option A identify measurable expectations concerning accuracy, latency, throughput, cost, privacy, compliance, escalation, and acceptable failure behavior before the solution is scoped.
Option D converts those expectations into production gates. Success criteria must be explicit enough to evaluate empirically, while failure thresholds must determine when the design requires remediation, human review, or rejection. Anthropic's evaluation guidance states that successful LLM applications begin with defined success criteria and evaluations that measure performance against them. It characterizes strong criteria as specific, measurable, achievable, and relevant. Define Success Criteria and Build Evaluations Option B prematurely selects a model before the workload profile is known. Option C can supplement discovery, but reaction-driven prototyping cannot replace documented requirements and acceptance criteria.
Option E postpones essential constraints until architectural commitments may already have been made.
The discovery output should create traceability from stakeholder objectives to requirements, evaluation metrics, architecture decisions, and deployment gates.
Study Guide references/topics: Structured discovery; stakeholder workshops; nonfunctional requirements; success criteria; failure thresholds; production readiness gates.
質問 # 123
During an architectural review, the security team identifies a risk that adversarial content injected into retrieved documents could manipulate the model's behavior.
Which mitigation most directly addresses this threat?
正解：A
解説：
The scenario describes indirect prompt injection: malicious instructions are embedded in third-party content that Claude processes as retrieved evidence. Option A addresses the threat at the appropriate boundary by classifying retrieved material as untrusted, screening it before use, and validating the model's output before it can influence downstream actions.
Anthropic recommends placing untrusted third-party content in clearly identified tool-result structures, explicitly instructing Claude that retrieved content cannot override system or user instructions, screening tool output for injection attempts, and applying output validation. Mitigate Jailbreaks and Prompt Injections Citations in Option B improve factual traceability but do not prevent embedded instructions from attempting to redirect model behavior. An outbound destination allow-list in Option C limits the damage that a compromised agent can cause through tool calls, but it does not directly detect or neutralize the injected content. Option D is valuable for detecting behavioral regression after a model change, not for controlling a specific malicious document during runtime.
A production implementation should combine content provenance, structural separation, injection screening, least-privilege tools, output-schema validation, and human confirmation for consequential actions.
Study Guide references/topics: Indirect prompt injection; untrusted retrieved content; input classification; output validation; layered runtime guardrails.
質問 # 124
You are compiling a diagnostic toolkit for Claude Code operational issues.
Which two diagnostic actions belong in the toolkit? (Select two.)
Each correct answer presents a complete solution.
正解：B、D
解説：
A minimal reproduction and MCP status inspection are foundational diagnostic actions because they produce direct, interpretable evidence. A minimal reproduction removes unrelated prompts, tools, settings, hooks, and repository state while changing one variable at a time. This determines whether the failure follows a particular configuration, server, command, model request, or project artifact. It also creates a reproducible evidence package if escalation later becomes necessary.
MCP inspection addresses a common Claude Code failure domain. `claude mcp list` displays configured servers and their health states, including connected, authentication-required, pending-approval, and failed-to- connect conditions. `claude mcp get < name > ` provides server-specific details, while `/mcp` exposes status within an interactive session. These checks distinguish model behavior from transport, authentication, configuration, and workspace-trust failures.
Increasing temperature reduces repeatability and introduces additional variation, making root-cause isolation more difficult. Filing a support ticket before collecting local evidence reverses the appropriate diagnostic sequence. An immediate rollback may eventually be used as a controlled version comparison, but it changes the environment before a baseline has been captured and is therefore not a primary toolkit action.
Study Guide references/topics: [Managing and diagnosing MCP servers](https://docs.anthropic.com/en/docs
/claude-code/mcp); minimal reproducible cases; controlled-variable troubleshooting; authentication and connection-state diagnostics.
質問 # 125
You are listing characteristics of robust guardrail design for an enterprise deployment.
Which two characteristics belong on the list? (Select two.)
Each correct answer presents a complete solution.
正解：A、B
質問 # 126
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