This course equips data professionals and developers with the skills to design and implement AI-enabled database solutions using Microsoft technologies. Learners will explore how to integrate artificial intelligence capabilities directly into database platforms such as SQL Server, Azure SQL, and Microsoft Fabric. The course focuses on building intelligent applications using vector search, embeddings, and AI-driven query capabilities, enabling organizations to unlock advanced insights from their data. Participants will gain hands-on experience developing modern, AI-powered database solutions aligned with emerging enterprise use cases. Develop AI-Enabled Database Solutions (DP-800) Benefits Course Benefits Pain Points specific to each course Difficulty integrating AI into existing database systems Lack of knowledge around vector search and embeddings Challenges building intelligent, data-driven applications Limited understanding of AI capabilities within SQL platforms Benefits like certification, exam included, ACIC Build AI-enabled database solutions using Microsoft technologies Gain hands-on experience with vector search and embeddings Learn how to integrate AI into enterprise data platforms Improve application intelligence and search capabilities Stay current with emerging AI + data architecture trends Align skills with modern AI-driven development practices Prerequisites Experience with SQL and relational databases Basic understanding of cloud data platforms Familiarity with application development concepts Exam Information Microsoft Certified: SQL AI Developer Associate (beta) - Certifications | Microsoft Learn Who should attend Developers building AI-powered applications Data professionals modernizing database solutions Engineers integrating AI into enterprise systems IT professionals exploring AI-driven data platforms AI Enabled Database Training Outline Learning Objectives Design and implement database objects with SQL Understand 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 Create and maintain database objects Implement programmability objects with SQL Create views Create stored procedures Create scalar functions Create table-valued functions Create triggers Choose when to use each option Implement programmability objects in SQL Server Write advanced T-SQL code 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 Work with JSON functions Implement SQL solutions by using AI-assisted tools Describe AI-assisted development tools 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) options Create and configure GitHub Copilot instruction files Connect to MCP server endpoints, including SQL Server and Fabric Lakehouse Configure AI-assisted tools for database development Implement data security and compliance with SQL 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 Implement security features Optimize database performance Recommend database configurations Preserve data integrity with transaction isolation levels and concurrency controls Evaluate query performance with execution plans and DMVs Monitor and tune queries with Query Store and Query Performance Insight Identify and resolve blocking and deadlocks Optimize query performance Implement CI/CD by using SQL Database Projects 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 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 Handle changes with event-driven patterns Configure Data API Builder for solutions 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 database workloads Generate and maintain embeddings Generate and update embeddings in Azure SQL Database Design and implement intelligent search with SQL Choose an intelligent search approach Implement full-text search Prepare SQL for vector search Implement vector search query patterns Implement hybrid search and ranking Implement intelligent search with full-text, vector, and hybrid queries 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 Implement a RAG solution
