Tài liệu ôn CCAR-P

Thiết kế, quản trị và vận hành giải pháp Claude ở quy mô doanh nghiệp.

• 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