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CCAR-P · Study Guide

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1.1 Translate business problems into Claude-based AI solutions 1.2 Design end-to-end architectures (input → processing → output → feedback loops)1.3 Select appropriate architectural patterns (workflow, agentic, augmented LLM)1.4 Design multi-agent systems and orchestration strategies 1.5 Apply decomposition techniques for complex problem solving 1.6 Align solutions to business value pillars (efficiency, transformation, productivity, cost, performance SLAs)

2.1 Select appropriate Claude models based on trade-offs 2.2 Design system prompts, templates, and guardrails 2.3 Apply prompt engineering techniques (zero-shot, few-shot, chain-of-thought)2.4 Optimize context windows and manage token usage 2.5 Implement prompt reuse strategies (caching, modular prompts, Skills)

3.1 Evaluate tool/agent configuration for capability bloat 3.2 Analyze authentication and authorization requirements to identify security gaps 3.3 Evaluate accuracy-latency trade-offs and justify configuration decisions 3.4 Analyze observability challenges and select monitoring strategies at scale 3.5 Design a RAG pipeline with appropriate chunking and indexing strategies 3.6 Apply retrieval strategies matched to data shape and query pattern 3.7 Evaluate connection protocols and select the appropriate integration mechanism (MCP, API/CLI, agent-to-agent)3.8 Evaluate progressive discovery vs. monolithic context strategy

4.1 Define evaluation metrics (accuracy, latency, cost, safety, security)4.2 Design evaluation datasets and test frameworks using mixed methodologies 4.3 Conduct A/B testing and iterative improvements 4.4 Diagnose system issues (prompt failure, hallucinations, model mismatch)4.5 Optimize token usage, latency, and cost-performance trade-offs 4.6 Monitor system performance using logging and observability tools

5.1 Implement guardrails and safety controls 5.2 Identify risks, limitations, and failure modes of LLM systems 5.3 Apply human-in-the-loop validation strategies 5.4 Ensure compliance with regulations (e.g., GDPR, HIPAA, FedRAMP)5.5 Address ethical AI considerations (bias, fairness, transparency)

6.1 Conduct structured discovery and requirement gathering 6.2 Communicate architectural decisions and trade-offs 6.3 Manage stakeholder feedback loops and expectation alignment (including SLAs)6.4 Document architectures and provide implementation guidance 6.5 Support lifecycle phases (discovery, design, handoff, monitoring, iteration)

7.1 Configure Claude tools and environments for teams (e.g., Claude Code)7.2 Improve developer workflows using AI-assisted tooling 7.3 Support debugging and operational issue resolution

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Domain 4

Evaluation, Testing & Optimization

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