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Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Integration | 19% | - Enterprise system integration - Claude integration mechanisms
|
| Evaluation, Testing & Optimization | 16% | - Cost and performance optimization - System issue diagnosis - Evaluation framework design - Evaluation metrics and datasets - Production monitoring and optimization - A/B testing |
| Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Guardrails - Claude model selection and trade-offs - Context window optimization - System prompts and prompt templates |
| Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Architectural patterns
- Alignment with business value, cost, performance, and SLAs - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration |
| Governance, Safety & Risk Management | 14% | - Security and risk management - Ethical AI considerations - AI safety and guardrails - Regulatory and compliance requirements - Human-in-the-loop validation |
| Stakeholder Communication & Lifecycle Management | 14% | - Stakeholder management - Service-level agreements - Solution lifecycle management - Architecture documentation - Discovery and requirements gathering - Communicating architectural decisions |
| Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - Debugging and operational issue resolution - AI-assisted developer workflows - Developer enablement |
Anthropic Claude Certified Architect - Professional Sample Questions:
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?
- A. Use a separate model call to summarize all 60 tool definitions before each user turn.
- B. Increase the maximum context length and load all 60 tool definitions on every request, accepting the higher token cost and latency as necessary for full capability.
- C. Apply progressive tool discovery so a curated initial subset is exposed and additional tools are loaded on demand based on the task.
- D. Hard-code a fixed set of five tools per request to reduce token usage, regardless of whether those tools are relevant to the current task.
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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.
- A. List the configured Model Context Protocol (MCP) servers and inspect server status to identify connection failures.
- B. Roll back to the previous Claude Code version immediately to confirm whether the issue is version specific.
- C. Reproduce the issue with a minimal reproduction case that isolates one variable at a time.
- D. File a support ticket with vendor support before any local reproduction or evidence collection.
- E. Increase the model sampling temperature so that intermittent issues surface more frequently for analysis.
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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?
- A. Opus, because every moderation decision requires maximum reasoning depth regardless of classification complexity.
- B. Sonnet, because larger general-purpose models are preferred even when workload latency requirements are strict.
- C. Sonnet with extended thinking enabled, because deeper reasoning should be applied to every moderation request to improve edge-case handling.
- D. Haiku, because its latency and cost profile align with high-volume classification workloads that require limited reasoning depth.
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A customer support team has proposed delegating customer refund decisions to a Claude-driven workflow with no human review for refunds under 50 USD. The team's reasoning is that small refunds are low-risk and human review would erase the efficiency gain.
Which Delegation-competency principle should guide your response?
- A. Delegation should be avoided entirely wherever financial transactions occur in the workflow.
- B. Delegation scope should reflect the type of risk involved, not the transaction size alone.
- C. Delegation should always include human review on every decision the workflow produces.
- D. Delegation scope should be set primarily by maximizing efficiency gains across the workflow.
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A senior architect is managing stakeholder expectations for a Claude-based reporting assistant midway through development. Stakeholders have escalating concerns about response latency.
Which two actions most directly address stakeholder expectation alignment in this situation? (Select two.)
- A. Revise the SLA definition collaboratively with stakeholders if current targets are not achievable given production constraints.
- B. Replace the current Claude model with a third-party model that may offer lower latency without evaluation.
- C. Present measured p50 and p95 latency baselines against the agreed SLA thresholds so stakeholders have accurate data.
- D. Communicate that latency concerns are a known LLM limitation and outside the architecture team's control.
- E. Pause all development and reallocate engineering resources entirely to latency optimization.
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