Design and implement database objects with SQL
This module covers designing and implementing various database objects including tables with different data types, specialized table types, indexes, constraints, and partitioning strategies. You’ll learn how to create and optimize database objects for modern SQL platforms.

• Introduction
• Understand your SQL Server-based platform choices
• Build effective tables
• Optimize with indexes
• Use specialized table types
• Enforce data integrity with constraints
• Manage JSON columns and indexes
• Partition tables for scale
• Exercise – Create and maintain database objects
• Module assessment

Implement programmability objects with SQL
Learn how to create and use views, stored procedures, scalar functions, table-valued functions, and triggers to build maintainable, secure, and efficient database solutions.

• Introduction
• Create views
• Create stored procedures
• Create scalar functions
• Create table-valued functions
• Create triggers
• Choose when to use each option
• Exercise: Implement programmability objects in SQL Server

Write advanced T-SQL code
Learn advanced T-SQL techniques including CTEs, window functions, JSON, regular expressions, fuzzy matching, graph queries, and error handling for SQL Server, Azure SQL, and Fabric.

• Introduction
• Organize queries with Common Table Expressions
• Apply window functions for analytics
• Process JSON data with built-in functions
• Match patterns with regular expressions
• Find approximate matches with fuzzy string functions
• Traverse relationships with graph queries
• Compare rows with correlated subqueries
• Handle errors with TRY…CATCH
• Exercise – Work with JSON functions
• Module assessment

Implement SQL solutions by using AI-assisted tools
Learn how to leverage GitHub Copilot and Fabric Copilot for AI-assisted database development across SQL Server, Azure SQL, and SQL databases in Microsoft Fabric.

• Introduction
• Describe AI-assisted development tools available for Microsoft SQL platforms
• Interpret security impact of using AI-assisted tools
• Enable GitHub Copilot and Fabric Copilot
• Configure model and Model Context Protocol (MCP) tool 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
• Module assessment

Implement data security and compliance with SQL
Learn how to protect sensitive data and meet compliance requirements by implementing encryption, masking, access controls, and auditing across Microsoft’s SQL platforms.

• Introduction
• Protect data with encryption
• Configure dynamic data masking
• Implement row-level security
• Manage permissions and secure access
• Implement auditing
• Configure secure access to AI services
• Secure data API endpoints
• Exercise: Implement security features
• Module assessment

Optimize database performance
Optimize Azure SQL Database performance by choosing the right service tier and managing concurrency with transaction isolation levels. Analyze queries with execution plans and DMVs. Use Query Store for plan management and diagnose blocking and deadlocks.

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

Integrate SQL solutions with Azure services
Create REST and GraphQL APIs for SQL databases using Data API Builder, deploy to Azure hosting services, and implement monitoring and event-driven change patterns.

• Introduction
• 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
• Handle changes with event-driven patterns
• Exercise – Configure Data API Builder for a product catalog
• Module assessment

Design and implement models and embeddings with SQL
Integrate AI models with Azure SQL Database using external models and built-in AI functions. Design effective embedding strategies and implement maintenance patterns to keep embeddings aligned with source data.

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

Design and implement intelligent search with SQL
Implement intelligent search capabilities in SQL Server and Azure SQL by combining traditional full-text search with semantic vector search. Establish a mental model for different search approaches, prepare SQL for vector-based search, and implement vector, hybrid, and ranking-based search patterns with performance considerations.

• Introduction
• Choose an intelligent search approach
• Implement full-text search
• Prepare SQL for vector search
• Implement vector search query patterns
• Implement hybrid search and ranking
• Exercise – Implement intelligent search with full-text, vector, and hybrid queries

Design and implement RAG with SQL
This module teaches you how to implement Retrieval Augmented Generation (RAG) using Azure SQL Database. You learn to identify appropriate RAG scenarios, prepare SQL results as LLM context, construct augmented prompts, and process model responses.

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