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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Security and risk management - Ethical AI considerations - AI safety and guardrails - Human-in-the-loop validation - Regulatory and compliance requirements |
| Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - Claude integration mechanisms
|
| Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Context window optimization - Prompt reuse and context engineering strategies - Guardrails - Claude model selection and trade-offs |
| Developer Productivity & Operational Enablement | 7% | - Developer enablement - Claude tooling configuration for teams - AI-assisted developer workflows - Debugging and operational issue resolution |
| Evaluation, Testing & Optimization | 16% | - A/B testing - Cost and performance optimization - Evaluation metrics and datasets - System issue diagnosis - Evaluation framework design - Production monitoring and optimization |
| Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Multi-agent systems and orchestration - Alignment with business value, cost, performance, and SLAs - Architectural patterns
- Translating business problems into Claude-based AI solutions |
| Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Solution lifecycle management - Stakeholder management - Communicating architectural decisions - Discovery and requirements gathering - Architecture documentation |
CCAR-PのAnthropic試験問題の高い合格率は98％〜100％であるため、正確かつ最新のCCAR-P試験トレントで市場に並ぶものがないと誇らしげに主張できます。 成功をもたらす当社の強みと、取得する意図のある認定を疑うことはありません。 CCAR-P実践教材を使用して、候補者の勝利をますます証明しています。CertShiken あなたは彼らのような勝者の一人になると信じています。 Claude Certified Architect - ProfessionalのCCAR-P学習教材を購入するだけで、より明るい未来を手にすることができます。
質問 # 88
A healthcare organization is evaluating two Claude-powered AI architectures for a clinical documentation assistant. Architecture X produces higher output quality scores but costs $0.18 per documentation session and averages 4.2 seconds per response. Architecture Y scores slightly lower on quality metrics but costs $0.09 per session and averages 2.1 seconds per response. The stated SLA requires responses under 3 seconds, and the annual volume is projected at 2 million documentation sessions.
Which evaluation approach correctly applies business value pillar analysis to this decision?
正解：D
解説：
Architecture X fails a stated production gate: its 4.2-second average exceeds the required three-second response SLA. It should therefore not remain a qualifying architecture unless it is redesigned and retested before selection. A promise to optimize after deployment is not evidence of compliance.
Architecture Y meets the latency requirement at 2.1 seconds. Its annual inference cost is $180,000 at two million sessions, compared with $360,000 for Architecture X-a difference of $180,000 annually. However, cost alone does not establish suitability. The organization must determine whether Y's lower quality score causes material omissions, correction effort, clinician dissatisfaction, documentation risk, or reduced workflow productivity.
Option C correctly treats the SLA as a threshold and then analyzes the remaining architecture across cost, efficiency, quality, and productivity. Option A ignores clinical quality. Option B disregards an explicit SLA failure. Option D may eventually be viable, but it introduces routing complexity and leaves unanswered how complex cases are detected, whether X's latency becomes acceptable for them, and how both paths are evaluated.
The final choice should be gated by clinical-quality evaluation, human review requirements, safety metrics, operational latency, and total annual cost.
Study Guide references/topics: Business-value pillars; performance gates; solution cost; workflow efficiency; quality trade-offs; healthcare architecture evaluation.
質問 # 89
You are presenting an architectural decision to a mixed audience that includes an executive sponsor and the engineering leads who will implement the decision.
Which presentation strategy best serves both audiences?
正解：D
質問 # 90
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
正解：
解説：
Explanation:
Function-level or section-level chunking for code modules - Code or Hierarchical Specifications Tree-aware chunking that follows code or specification hierarchy - Code or Hierarchical Specifications Per-record chunking where each record is one chunk - Heterogeneous Short Records Semantic chunking along clause or paragraph boundaries - Long Structured Documents Fixed-size chunking with overlap for short records of similar length - Heterogeneous Short Records Hierarchical chunking that mirrors document section structure - Long Structured Documents Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval
質問 # 91
You are a solution architect designing a Claude-based assistant with access to 60 internal tools across multiple business domains. Loading every tool definition on every request increases token usage and time to first response.
Which design pattern best addresses this issue without sacrificing capability breadth?
正解：C
解説：
Progressive tool discovery preserves the complete capability catalog while limiting initial context to a curated set of common tools and a discovery mechanism. When the task requires an additional capability, Claude searches the catalog and loads only the relevant tool definitions.
Anthropic's Tool Search mechanism implements this pattern through deferred loading. The context initially contains the search tool and any deliberately non-deferred tools; relevant definitions are expanded only when discovered. This reduces token consumption and helps maintain tool-selection accuracy in large catalogs.
Tool Search Tool
Option B adds a model call and still repeatedly processes all definitions, increasing latency and cost while potentially losing important schema details. Option C treats a larger context window as permission to waste context and does not address time-to-first-response overhead. Option D reduces capability breadth because the fixed five tools may be irrelevant to a given task.
The architect should provide clear tool and server descriptions, define which high-frequency tools remain immediately available, and evaluate discovery recall, selection accuracy, latency, and context savings across representative workflows.
Study Guide references/topics: Progressive discovery; deferred tool loading; MCP scaling; context optimization; tool-selection accuracy; on-demand capability loading.
質問 # 92
A Claude Architect is reviewing a post-deployment performance report for an AI-assisted legal-document summarization system. The report includes these observations:
* Average summarization time decreased from 47 minutes to 6 minutes per document.
* Associates spend less time on summaries, but overall billable output has not measurably changed.
* Infrastructure costs increased by 22% because redundant retry logic generated additional API calls.
* Some summaries require attorney correction, adding an average of 8 minutes of review per document.
Which analysis correctly attributes each observation to the appropriate business-value pillar?
正解：D
解説：
Observation 1 is a direct efficiency improvement because the time required for one summarization task fell substantially. Observation 2 shows that local time savings have not yet translated into higher organizational productivity or billable output. Observation 3 is explicitly a solution-cost problem caused by unnecessary API calls. Observation 4 introduces additional human correction time and therefore partially offsets the gross efficiency gain reported in Observation 1. Option D is the only answer that keeps these value dimensions distinct. Measuring only generation speed would overstate realized value because review effort, retry costs, and downstream output must also be included. A valid post-deployment assessment should calculate net cycle time, cost per accepted summary, correction rate, and resulting business output.
質問 # 93
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