Develop AI-Enabled Database Solutions (DP-800T00)

Course Duration

4 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

Experience with SQL and relational database concepts. Familiarity with Azure services and data development is recommended.

Course Description

This course provides students with the knowledge and skills to design and develop AI-enabled database solutions across Microsoft SQL platforms, including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. It covers modern data solutions that integrate structured and semi-structured data and incorporate AI features into scalable enterprise applications — including vector search, embeddings, RAG patterns, and AI-assisted development tools.

Learning Objectives

• Design and implement scalable database objects, indexes, and programmability in SQL
• Write advanced T-SQL code including CTEs, window functions, and graph queries
• Use AI-assisted tools including GitHub Copilot and MCP servers for SQL development
• Implement data security, encryption, row-level security, and compliance controls
• Build CI/CD pipelines for SQL Database Projects using GitHub Actions
• Integrate SQL solutions with Azure services using Data API Builder and event-driven patterns
• Generate and manage vector embeddings for AI-enabled search and retrieval
• Implement vector search, hybrid search, and RAG patterns using SQL databases

Course Outline

Module 1: Design and Implement Database Objects with SQL

• Understand SQL Server-based platform choices
• Build effective tables and optimize with indexes
• Enforce data integrity with constraints
• Manage JSON columns and partition tables for scale

Module 2: Implement Programmability Objects with SQL

• Create views, stored procedures, and scalar functions
• Create table-valued functions and triggers

Module 3: Write Advanced T-SQL Code

• Organize queries with Common Table Expressions
• Apply window functions for analytics
• Process JSON data and match patterns with regular expressions
• Traverse relationships with graph queries
• Handle errors with TRY...CATCH

Module 4: Implement SQL Solutions Using AI-Assisted Tools

• AI-assisted development tools for Microsoft SQL platforms
• Enable GitHub Copilot and Fabric Copilot
• Configure Model Context Protocol (MCP) tool options
• Connect to MCP server endpoints including SQL Server and Fabric Lakehouse

Module 5: Implement Data Security and Compliance with SQL

• Protect data with encryption and dynamic data masking
• Implement row-level security and manage permissions
• Configure secure access to AI services and data API endpoints
• Implement auditing

Module 6: Optimize Database Performance

• Evaluate query performance with execution plans and DMVs
• Monitor and tune queries with Query Store
• Identify and resolve blocking and deadlocks

Module 7: Implement CI/CD Using SQL Database Projects

• Create, build, and validate SQL Database Projects
• Configure source control and manage branching
• Implement CI/CD pipelines and testing strategies

Module 8: Integrate SQL Solutions with Azure Services

• Create configuration files for Data API Builder
• Define entities for REST and GraphQL
• Recommend Azure Monitor configurations
• Handle changes with event-driven patterns

Module 9: Design and Implement Models and Embeddings with SQL

• Evaluate models for SQL database workloads
• Create and manage external models in SQL
• Generate and maintain embeddings

Module 10: Design and Implement Intelligent Search with SQL

• Implement full-text search
• Prepare SQL for vector search
• Implement vector search query patterns and hybrid search

Module 11: Design and Implement RAG with SQL

• Identify RAG use cases and architecture
• Prepare retrieval context for augmentation
• Augment prompts with database context
• Generate and process RAG responses

Frequently Asked Questions