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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Guardrails - Claude model selection and trade-offs - Prompt reuse and context engineering strategies - Context window optimization - System prompts and prompt templates |
| Topic 2: Solution Design & Architecture | 17% | - Architectural patterns
- Translating business problems into Claude-based AI solutions - Decomposition techniques for complex problem solving - End-to-end architecture design - Alignment with business value, cost, performance, and SLAs |
| Topic 3: Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Stakeholder management - Architecture documentation - Solution lifecycle management - Communicating architectural decisions - Discovery and requirements gathering |
| Topic 4: Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - System issue diagnosis - Production monitoring and optimization - Evaluation framework design - Cost and performance optimization - A/B testing |
| Topic 5: Governance, Safety & Risk Management | 14% | - AI safety and guardrails - Human-in-the-loop validation - Ethical AI considerations - Regulatory and compliance requirements - Security and risk management |
| Topic 6: Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Developer enablement - AI-assisted developer workflows - Claude tooling configuration for teams |
| Topic 7: Integration | 19% | - Authentication and authorization analysis - Claude integration mechanisms
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Unsere Prüfungsunterlage zu Anthropic CCAR-P（Claude Certified Architect - Professional）enthältet alle echten, originalen und richtigen Fragen und Antworten. Die Abdeckungsrate unserer Unterlage (Fragen und Antworten) zu Anthropic CCAR-P（Claude Certified Architect - Professional）ist normalerweise mehr als 98%.
12. Frage
You are defining where human review must remain in a planned automated pipeline. Which placement reflects sound human-in-the-loop design?
Antwort: D
Begründung:
Sound human-in-the-loop design places review before a high-impact or irreversible action occurs. The reviewer must receive sufficient context and explicit approval criteria so that the checkpoint functions as a meaningful control rather than a ceremonial step. This is especially important when Claude outputs could initiate financial transactions, publish sensitive material, alter access rights, affect employment decisions, or trigger other consequential actions. Option A provides only retrospective auditing and cannot prevent harm.
Option C may support monitoring or quality measurement, but random sampling does not protect every case that requires mandatory approval, particularly when review criteria are undefined. Option D removes the accountability and intervention mechanism entirely. Therefore, B provides the strongest design by combining risk-based placement, human authority, and clearly defined verification requirements before execution.
13. Frage
You are compiling factors that should drive the choice between Model Context Protocol (MCP), direct API integration, and agent-to-agent handoff.
Which two factors belong on the list? (Select two.)
Each correct answer presents a complete solution.
Antwort: A,B
Begründung:
The interaction model and portability requirement are architectural decision drivers. MCP is appropriate when tools or data sources must be exposed through a standardized interface reusable across compatible AI clients.
Direct API integration is often preferable for a narrow, stateless, latency-sensitive call where the application already owns the integration logic. Agent-to-agent handoff is better suited to stateful or longer-running delegation between independently responsible agents. Documentation quality and team familiarity influence implementation effort but do not determine the correct integration pattern. Encryption in transit is a mandatory security property for production integration rather than a differentiating architectural requirement; each viable approach must provide it. Anthropic distinguishes direct Messages access from stateful managed- agent infrastructure, while MCP supplies a standardized tool integration layer. Claude Platform documentation
14. Frage
You are reviewing a peer's draft system prompt that contains contradictory instructions: one section says never to speculate beyond the supplied source, while another says to confidently fill in any gaps.
Which response is most appropriate?
Antwort: A
Begründung:
The prompt contains mutually incompatible behavioral requirements. The architect should resolve the contradiction explicitly so the model receives one coherent grounding policy. If the application requires source-supported answers, the prompt should prohibit unsupported completion, permit an insufficient- evidence response, and require citations or supporting passages. Adding another priority instruction leaves the underlying conflict in place and creates ambiguity about when speculation is permitted. Increasing temperature makes behavior more variable rather than more correct. Retaining both instructions produces inconsistent outputs across requests and model versions. Anthropic recommends clear, direct, specific prompting and notes that conflicting project instructions can cause Claude to select among them unpredictably. Prompting best practices ; project-instruction troubleshooting
15. Frage
You are reviewing instrumentation in a multi-agent system.
Which two findings constitute valid observability gaps in the instrumentation? (Select two.) Each correct answer presents a complete solution.
Antwort: A,E
Begründung:
Model identity and version are essential diagnostic dimensions. Without them, operators cannot correlate a behavioral change with a model migration, reproduce the conditions of a failed turn, compare performance among model versions, or distinguish model drift from prompt, retrieval, and application defects. Therefore, the omission described in Option D is a genuine observability gap.
Option E is equally significant. A multi-agent execution normally crosses orchestration, model, agent, MCP, and tool boundaries. If the request-scoped correlation identifier or distributed trace context is lost at any boundary, the resulting spans cannot reliably be reconstructed into one end-to-end transaction. Anthropic's monitoring guidance explains that distributed tracing links a user prompt to the API requests and tool executions it initiates; it also documents propagation of W3C trace context to supported subprocesses and outbound MCP requests. Claude Code Monitoring Options A and B describe healthy telemetry coverage rather than gaps. Option C is also appropriate because recording tool interactions supports investigation, while redaction reduces the risk of exposing sensitive data in logs.
Study Guide references/topics: Integration-observability challenges at scale; distributed tracing; correlation identifiers; model-version attribution; privacy-aware tool telemetry.
16. Frage
A Claude architect at a health services organization is defining evaluation metrics for a clinical-summary pipeline. The pipeline must remain within a per-query cost ceiling and must never surface patient data to unauthorized roles.
Which two metrics directly address these requirements? (Select two.)
Antwort: B,D
Begründung:
The evaluation metrics must map directly to the two stated requirements. Role-based access-control enforcement rate, tested against a red-team dataset, measures whether requests from unauthorized identities, roles, or access paths are consistently denied. It converts the privacy and authorization requirement into an observable security metric and exposes bypass conditions that ordinary functional tests may miss. Because the requirement says patient data must never reach unauthorized roles, the acceptance threshold should be effectively 100% enforcement across representative and adversarial cases.
Per-query token cost directly measures compliance with the financial constraint. The metric should include uncached input, cache reads and writes where applicable, output tokens, thinking tokens, and any other billable components attributed to the request. Results can then be compared with the defined ceiling across normal, boundary, and worst-case inputs.
Anthropic's evaluation guidance identifies privacy preservation and price as distinct success criteria and emphasizes that criteria must be specific, measurable, and relevant to the application. Define Success Criteria and Build Evaluations BLEU may assess textual similarity but does not test authorization or cost. Throughput and percentile latency are valuable operational metrics, yet neither directly answers the two requirements in the scenario.
Study Guide references/topics: Multidimensional evaluation; privacy preservation; authorization testing; red- team datasets; token-cost measurement; requirement-to-metric traceability.
17. Frage
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