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

Who Should Attend?

Data professionals who want to learn about designing and developing AI-enabled database solutions across Microsoft’s SQL platforms, including SQL Server, Azure SQL and SQL databases in Microsoft Fabric. This role develops database solutions that include both structured and semi-structured data and integrates AI features into modern and highly scalable enterprise applications.

Design and implement database objects with SQL

Understand your SQL Server-based platform choices Build effective tables Optimize with indexes Use specialised table types Enforce data integrity with constraints Manage JSON columns and indexes Partition tables for scale

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

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

Implement SQL solutions by using AI-assisted tools

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

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

Optimise 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

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

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

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 for SQL database workloads

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

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