In this Claude Certified Architect Professional (CCAR-P) Certification Prep course, you'll learn:
Domain 1: Solution Design & Architecture
- Translate business problems into Claude-based AI solutions
- Design end-to-end architectures (input → processing → output → feedback loops)
- Select appropriate architectural patterns (workflow, agentic, augmented LLM)
- Design multi-agent systems and orchestration strategies
- Apply decomposition techniques for complex problem solving
- Align solutions to business value pillars (efficiency, transformation, productivity, cost, performance SLAs)
Domain 2: Claude Models, Prompting & Context Engineering
- Select appropriate Claude models based on trade-offs
- Design system prompts, templates, and guardrails
- Apply prompt engineering techniques (zero-shot, few-shot, chain-of-thought)
- Optimize context windows and manage token usage
- Implement prompt reuse strategies (caching, modular prompts, Skills)
Domain 3: Integration
- Evaluate tool/agent configuration for capability bloat
- Analyze authentication and authorization requirements to identify security gaps
- Evaluate accuracy-latency trade-offs and justify configuration decisions
- Analyze observability challenges and select monitoring strategies at scale
- Design a RAG pipeline with appropriate chunking and indexing strategies
- Apply retrieval strategies matched to data shape and query pattern
- Evaluate connection protocols and select the appropriate integration mechanism (MCP, API/CLI, agent-to-agent)
- Evaluate progressive discovery vs. monolithic context strategy
Domain 4: Evaluation, Testing & Optimization
- Define evaluation metrics (accuracy, latency, cost, safety, security)
- Design evaluation datasets and test frameworks using mixed methodologies
- Conduct A/B testing and iterative improvements
- Diagnose system issues (prompt failure, hallucinations, model mismatch)
- Optimize token usage, latency, and cost-performance trade-offs
- Monitor system performance using logging and observability tools
Domain 5: Governance, Safety & Risk Management
- Implement guardrails and safety controls
- Identify risks, limitations, and failure modes of LLM systems
- Apply human-in-the-loop validation strategies
- Ensure compliance with regulations (e.g., GDPR, HIPAA, FedRAMP)
- Address ethical AI considerations (bias, fairness, transparency)
Domain 6: Stakeholder Communication & Lifecycle Management
- Conduct structured discovery and requirement gathering
- Communicate architectural decisions and trade-offs
- Manage stakeholder feedback loops and expectation alignment (including SLAs)
- Document architectures and provide implementation guidance
- Support lifecycle phases (discovery, design, handoff, monitoring, iteration)
Domain 7: Developer Productivity & Operational Enablement
- Configure Claude tools and environments for teams (e.g., Claude Code)
- Improve developer workflows using AI-assisted tooling
- Support debugging and operational issue resolution
