Develop AI-enabled database solutions (DP-800T00)

Design and implement database objects using SQL

• Understand SQL Server–based platform options
• Create efficient tablesOptimize using indexes
• Use specialized table types
• Ensure data integrity with constraints
• Manage JSON columns and their indexesPartition tables for scalability
• Exercise – Create and maintain database objects

Implement programmable objects using SQL

• Create views
• Create stored procedures
• Create scalar functions
• Create table‑valued functions
• Create triggers
• Choose when to use each option
• Exercise – Implement programmable objects in SQL Server

Write advanced T‑SQL code

• Organize queries using common table expressions (CTEs)
• Apply window functions for analytics
• Process JSON data using built‑in functions
• Perform pattern matching with regular expressions
• Find approximate matches using fuzzy‑matching functions
• Traverse relationships using graph queries
• Compare rows using correlated subqueries
• Handle errors using TRY…CATCH
• Exercise – Working with JSON functions

Implement SQL solutions using AI‑assisted tools

• Describe AI‑assisted development tools available for Microsoft SQL platformsInterpret the security impact of using AI‑assisted tools
• Enable GitHub Copilot and Fabric Copilot
• Configure the model and Model Context Protocol (MCP) options in a GitHub Copilot or Fabric Copilot chat session
• Create and configure GitHub Copilot instruction files
• Connect to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse
• Exercise – Configure AI‑assisted tools for database development

Implement data security and compliance with SQL

• Protect data using encryptionConfigure dynamic data maskingImplement row‑level security (RLS)
• Manage permissions and secure accessImplement auditing
• Configure secure access to AI services
• Secure data API endpoints
• Exercise – Implement security features

Optimize database performance

• Recommend database configurations
• Preserve data integrity using transaction isolation levels and concurrency controls
• Evaluate query performance using execution plans and DMVs
• Monitor and optimize queries using Query Store and Query Performance Insight
• Identify and resolve blocking and deadlocks
• Exercise – Optimize query performance

Implement CI/CD using SQL Database projects

• Create, build, and validate SQL Database projects
• Configure source control and manage reference data
• Manage branches, pull requests, and conflict resolution
• Detect and resolve schema drift
• Implement CI/CD pipelines
• Design and implement a testing strategy
• Exercise – Implement CI/CD using SQL Database projects

Integrate SQL solutions with Azure services

• Create configuration files for Data API Builder
• Define entities for REST and GraphQL
• Expose database objects, stored procedures, and views
• Explore deployment options for Data API Builder
• Recommend Azure Monitor configurations
• Manage changes using event‑driven models
• Exercise – Configure Data API Builder for a product catalog

Design and implement models and embeddings with SQL

• Understand and evaluate models for SQL database workloads
• Create and manage external models in SQL
• Design embeddings for SQL workloadsGenerate and maintain embeddings for SQL workloads
• Exercise – Generate and update embeddings in Azure SQL Database

Design and implement intelligent search with SQL

• Choose an intelligent search approachImplement full‑text search
• Prepare SQL for vector search
• Implement vector search query patternsImplement hybrid search and result ranking
• Exercise – Implement intelligent search using full‑text, vector, and hybrid queries

Design and implement RAG with SQL

• Identify RAG use cases and architecture
• Prepare retrieval context for augmentation
• Enrich prompts with database context
• Generate and process RAG responses
• Exercise – Implement a RAG solution