Foundation Model Integration, Data Management, and Compliance
GenAI Solution Architecture and Model Selection
Translate business and technical requirements into a production GenAI architecture, validate assumptions with a proof of concept, and select foundation models by measured capability, latency, cost, context, modality, and regional availability.
Concepts
- Start with measurable business outcomes, data constraints, risk tolerance, latency targets, and operational requirements.
- Evaluate models against representative prompts and domain-specific quality criteria instead of relying on a generic leaderboard.
- Decouple application logic from model providers so routing, fallback, and model replacement do not require broad application changes.
- Use cross-Region inference, circuit breakers, and graceful degradation when availability requirements exceed one model endpoint.
- Distinguish intelligent prompt routing, which selects between eligible models by predicted quality, from inference profiles, which route a requested model across allowed Regions.
Exam tips
- Prefer representative evaluation data over headline benchmark scores.
- Use a proof of concept to retire feasibility and quality risks before production investment.
- Separate model routing from business logic to support provider and version changes.