JITA Learning · Microsoft · Field Guide
DP-800 · Developing AI-Enabled Database Solutions
Build working judgment for AI-enabled database solution design.
What you should expect
Build working judgment for AI-enabled database solution design.
This Field Guide develops a working mental model of AI-enabled database solution design. You will connect connect AI-enabled database workloads to data architecture, application access, APIs, and automation, choose vector, embedding, search, model, and retrieval patterns that fit the workload, reason about MCP, REST, GraphQL, stored procedures, external models, and application integration boundaries, and design security, identity, source control, deployment, and data-access controls. You are not just memorizing feature names; you are learning what owns the requirement, what can fail, and how to verify the outcome.
What you will learn
- connect AI-enabled database workloads to data architecture, application access, APIs, and automation
- choose vector, embedding, search, model, and retrieval patterns that fit the workload
- reason about MCP, REST, GraphQL, stored procedures, external models, and application integration boundaries
- design security, identity, source control, deployment, and data-access controls
- recognize why one technically plausible alternative loses under the stated requirement
- apply Changed Fact reasoning and verify the design or implementation through operational evidence
Decision skills you will practice
- identify the governing requirement
- compare the nearest plausible alternative
- apply Changed Fact reasoning
- trace prerequisites, dependencies, and failure paths
- verify configured state, effective state, runtime outcome, and negative-path evidence
Who this is for
database developers, data engineers, and application architects building AI-enabled solutions on Microsoft database platforms
Use this when you need to learn the architecture, mechanics, dependencies, tradeoffs, failure paths, and verification logic behind the certification.
Decisions you will learn to distinguish
- ANN vs exact vector-search requirement
- database-owned AI operation vs external application/service orchestration
- successful model or API call vs verified data and application outcome
Why JITA is different
Microsoft explains the platform. JITA teaches the decision.
Microsoft Learn explains the services, features, and documented behavior that support AI-enabled database solution design. JITA connects those capabilities into decisions: what owns the requirement, what the nearest alternative is, what changes when one fact changes, and what evidence proves the implementation worked.
How to choose:
Use the Field Guide to build understanding. Use the Exam Preparation Guide to test whether that understanding survives exam-style decisions.