DP-800: Exam Objectives
For the complete and official list of exam objectives, refer to the Microsoft study guide:
Study guide for Exam DP-800: Developing AI-Enabled Database Solutions
Source Scope
This page mirrors the official Microsoft Learn study guide wording and weighting published for DP-800, including the Skills measured as of March 12, 2026.
Audience Profile
According to Microsoft, candidates for DP-800 should have subject matter expertise in designing and developing AI-enabled database solutions across:
- Microsoft SQL Server
- Azure SQL
- SQL databases in Microsoft Fabric
You are also expected to be comfortable with:
- T-SQL development
- database development on Microsoft SQL platforms
- CI/CD practices in GitHub
- AI-assisted development tools
- AI concepts such as embeddings, vectors, and models
Exam Weighting
| Domain | Weight | Focus |
|---|---|---|
| Design and develop database solutions | 35-40% | Database objects, advanced T-SQL, programmability, AI-assisted SQL development |
| Secure, optimize, and deploy database solutions | 35-40% | Security, compliance, performance, SQL projects, testing, DAB, Azure integration |
| Implement AI capabilities in database solutions | 25-30% | External models, embeddings, intelligent search, vector functions, RAG |
Skills Measured
Design and develop database solutions (35-40%)
- Design and implement database objects Create and manage tables, indexes, constraints, specialized table types, JSON columns and indexes, and SEQUENCES
- Implement programmability objects Create and manage views, functions, stored procedures, and triggers
- Write advanced T-SQL code Use CTEs, correlated subqueries, window functions, JSON functions, regular expressions, graph queries, fuzzy functions, and robust error handling
- Design and implement SQL solutions by using AI-assisted tools Use GitHub Copilot and Fabric Copilot, instruction files, model options, MCP servers and tools, and secure AI-assisted workflows
Secure, optimize, and deploy database solutions (35-40%)
- Implement data security and compliance Use column-level encryption, Always Encrypted, Dynamic Data Masking, Row-Level Security, auditing, object-level permissions, and passwordless access patterns
- Optimize database performance Analyze execution plans, use DMVs and Query Store, troubleshoot blocking and deadlocks, and recommend database configurations
- Implement CI/CD by using SQL Database Projects Work with SDK-style projects, testing strategy, unit and integration tests, reference data, schema drift, conflict resolution, branching, and secure secret handling
- Integrate SQL solutions with Azure services Configure Data API builder, REST and GraphQL endpoints, data caching, Azure Functions, Azure Logic Apps, CES, CDC, Change Tracking, and Azure Monitor configurations including Application Insights and Log Analytics
Implement AI capabilities in database solutions (25-30%)
- Design and implement models and embeddings Evaluate model options, create and manage external models, design embedding pipelines, choose chunking strategy, and manage embedding refresh
- Design and implement intelligent search Implement full-text, vector, and hybrid search; use VECTOR_SEARCH, VECTOR_DISTANCE, VECTOR_NORMALIZE, and VECTORPROPERTY; understand vector indexes, metrics, ANN, ENN, and RRF; evaluate retrieval performance
- Design and implement retrieval-augmented generation (RAG) Retrieve context, shape prompts, invoke external model endpoints with SQL, parse responses, and keep answers grounded in current enterprise data
Study Use
Use this page as the coverage checklist. Use the domain notes for explanations and examples: