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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks |
| Topic 2: 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 3: Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Topic 4: Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Implement iterative improvement pipelines |
| Topic 5: Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment |
| Topic 6: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies |
| Topic 7: Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools |
CCAR-P学習資料の内容はすべて、Anthropic長年にわたる試験の概要と業界の発展動向に基づいて、ShikenPASS業界の専門家によって編集されています。 CCAR-P試験ガイドは、単なるテスト問題のパッチワークではなく、独自のシステムと階層レベルを備えているため、ユーザーは効果的に改善できます。 CCAR-P学習資料には、さまざまな被験者の特性と範囲に応じて試験の専門家が作成したテストペーパーが含まれています。 また、CCAR-P試験の質問で勉強すると、Claude Certified Architect - Professional試験に合格することになります。
質問 # 42
A Claude architect observes that after a recent model-version upgrade, grounded responses began including claims not supported by the retrieved source documents.
Which mitigation is most directly targeted at this failure mode?
正解：D
解説：
The observed failure is a groundedness defect: the response contains claims that cannot be traced to retrieved evidence. Option A directly constrains generation to the supplied sources, requires visible attribution, and verifies that each material claim has supporting text. Unsupported claims should be removed, qualified, or replaced with an explicit statement that the available sources are insufficient. Regression evaluation in Option B may detect the failure but does not itself prevent unsupported claims. OAuth addresses identity and authorization, while input classifiers address malicious or unsafe retrieved content; neither verifies factual support. Anthropic recommends citations, supporting quotations, and post-generation claim verification to reduce hallucinations in document-grounded applications. Reducing hallucinations
質問 # 43
You are designing a content moderation classifier that processes high volumes of user-generated comments under a tight per-message latency budget using well-defined classification labels.
Which model selection best aligns with the workload?
正解：B
解説：
Haiku is the appropriate starting point because the workload is high-volume, latency-sensitive, and based on a stable closed set of moderation labels. These characteristics favor a fast, cost-efficient model capable of consistent classification without incurring the additional inference time and expense associated with deeper reasoning.
Anthropic's model-selection guidance requires architects to balance capability, speed, and cost rather than automatically selecting the most capable model. Its content-moderation guidance specifically identifies Haiku as a cost-effective option for processing moderation workloads at substantial scale. Choosing the Right Model
, Content Moderation
Opus is disproportionate to a routine closed-set classification problem. Sonnet may become justified if evaluation demonstrates that Haiku fails materially on complex policy distinctions, multilingual ambiguity, or adversarial edge cases, but it should not be selected merely because it is larger. Enabling extended thinking on every request would further increase latency and token consumption without evidence that the additional reasoning improves the defined success metrics. The correct architectural practice is to establish a representative moderation evaluation set, validate Haiku against accuracy and safety thresholds, and escalate only the cases that genuinely need deeper reasoning.
Study Guide references/topics: Model selection; capability-latency-cost trade-offs; classification workloads; evaluation-driven routing; moderation architecture.
質問 # 44
You are compiling guardrail tactics for a customer-facing assistant.
Which two tactics belong on the guardrail list?
Each correct answer presents a complete solution.
正解：B、E
解説：
Option C introduces a deterministic validation boundary between probabilistic model output and downstream execution. Structured validation can reject missing fields, invalid types, unauthorized actions, malformed arguments, and values outside approved enumerations before any tool or business process acts on the response. Anthropic recommends Structured Outputs when guaranteed schema conformance is required.
Increase Output Consistency
Option D implements defense in depth. Prompt-level instructions establish intended behavior, while runtime classifiers, content filters, policy engines, output validators, tool permissions, and human confirmation gates provide controls outside the model. Anthropic's guardrail guidance recommends combining safeguards rather than depending on one mechanism. Mitigate Jailbreaks and Prompt Injections Option A creates a bypass mechanism that adversaries may discover or imitate. Option B may alter output variability but is not a security boundary and cannot guarantee policy compliance. Option E places complete reliance on a system prompt, which remains vulnerable to ambiguity, conflicting inputs, model errors, and indirect prompt injection.
Guardrails should be risk-based, independently enforceable where possible, observable, tested against adversarial cases, and proportional to the consequences of downstream actions.
Study Guide references/topics: Layered guardrails; structured validation; runtime content checks; prompt injection; deterministic enforcement; defense in depth.
質問 # 45
You are a solution architect evaluating candidate use cases for a Claude-based program.
For each scenario, select Yes if Claude is appropriate as the primary solution at the architectural level.
Otherwise, select No.
正解：
解説：
Explanation:
Drafting first-pass investigative reports from semi-structured incident logs for analyst review - Yes Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline - No Long-context contract review with structured clause extraction and deviation flagging - Yes Replacing a vector index for semantic retrieval over a multi-million-document corpus - No Routing inbound support tickets into 30 categories with reasoning for the selected route - Yes Claude is well suited to language-intensive analysis, summarization, extraction, classification, and drafting.
Investigative-report drafting, contract analysis, and support-ticket routing all require interpretation of unstructured or semi-structured language and can incorporate human review. A sub-50-millisecond fraud- scoring path requires deterministic, specialized real-time processing and should not place LLM inference on the critical path. Claude also does not replace the retrieval index used to search millions of documents; it consumes the results produced by that infrastructure. The correct architectural decision separates tasks requiring semantic language reasoning from deterministic computation and information-retrieval infrastructure. Model selection and evaluation must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.
質問 # 46
You are classifying token-management tactics by where in the request lifecycle each tactic applies.
For each tactic, select where in the request lifecycle it applies: Input Preparation , Prompt Construction , or Output Handling .
正解：
解説：
Explanation:
Input Preparation reduces the amount of unnecessary information before the request is assembled.
Summarizing older conversation history and trimming retrieved passages preserve relevant context while reducing token consumption. Prompt Construction determines how prepared content is organized for the model. Stable instructions and repeated reference material should appear in a cacheable prefix, while variable per-request content follows afterward, improving prompt-cache reuse and lowering processing cost. Output Handling begins after Claude generates the response. Structured output must be validated against the required schema before downstream systems consume it. Once validated, the result can be persisted for later processing, audit, or compliance purposes. Correctly separating these tactics prevents validation and persistence from being confused with input optimization while ensuring that caching decisions are implemented during prompt assembly rather than during retrieval or post-processing.
質問 # 47
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AnthropicのCCAR-P試験準備は、テストヒット率が高いため、98％〜100％の合格率です。 したがって、当社のCCAR-P学習教材は効果的であるだけでなく、有用でもあります。 誰もが知っているように、時間は誰にとっても非常に重要です。 一部の候補者は、自分の仕事や家族で非常に忙しいです。 CCAR-P試験の審査に時間をかけることは非常に困難です。 ただし、CCAR-P試験の教材を使用する場合、学習する時間はほとんどなく、Claude Certified Architect - Professional合格率は高くなります。 CCAR-P学習教材はあなたの信頼に値します。
CCAR-P試験勉強書: https://www.shikenpass.com/CCAR-P-shiken.html