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DP-800T00: Develop AI-enabled database solutions Training

Learn to build database solutions with AI capabilities like vector search, embeddings, and AI-assisted development using SQL Server, Azure SQL, and Microsoft Fabric.

📘 SQL 👥 123 Enrolled ⏱️ 3 Days 💼 Intermediate Level ⭐ 5 | 3 Reviews

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| DP-800T00: Develop AI-enabled database solutions

Course Overview

This course covers how to design, build, and optimize database solutions with AI features across Microsoft's SQL platforms: SQL Server, Azure SQL, and Microsoft Fabric. You'll work with structured and semi-structured data while getting practical experience with vector search, embeddings, and Retrieval-Augmented Generation (RAG). It also addresses performance tuning, security, CI/CD pipelines, and AI-assisted development through tools like GitHub Copilot. When you finish, you'll be able to build scalable, production-grade database applications that make good use of AI.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date Time Duration Mode Price
Apr 29, 2026 9:00 am - 5:00 pm 3 Days Online
$1595
May 12, 2026 9:00 am - 5:00 pm 3 Days Online
$1595
May 29, 2026 9:00 am - 5:00 pm 3 Days Online
$1595
Jun 12, 2026 9:00 am - 4:00 pm 3 Days Online
$1595
Jun 30, 2026 9:00 am - 5:00 pm 3 Days Online
$1595
+ View more schedules

What you will learn

  • Design and implement scalable database solutions across SQL platforms
  • Develop advanced T-SQL queries using CTEs, window functions, and JSON
  • Integrate AI capabilities such as embeddings and vector search into databases
  • Implement database security, compliance, and performance optimization techniques
  • Build and deploy database solutions using CI/CD pipelines and DevOps practices
  • Develop AI-powered applications using RAG and AI-assisted tools like GitHub Copilot

Who Should Attend This Course?

  • Data Engineer
  • Data Analyst
  • Database Administrator
  • Data Scientist
  • Software Developer
  • Database Developer

Prerequisites

Required

  • Basic understanding of database concepts (tables, joins, transactions)
  • Experience writing T-SQL queries

Recommended

  • Familiarity with SQL Server or Azure SQL Database
  • Basic knowledge of AI/ML concepts
  • Understanding of Git and CI/CD workflows

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📘 DP-800T00: Develop AI-enabled database solutions Outline

Design and implement database objects with SQL

  • 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

Implement programmability objects with SQL

  • 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

  • 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

Implement SQL solutions using AI-assisted tools

  • 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
  • Create and configure GitHub Copilot instruction files
  • Connect to MCP server endpoints (SQL Server and Fabric Lakehouse)
  • Exercise - Configure AI-assisted tools for database development

Implement data security and compliance with SQL

  • 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

Optimize database performance

  • Introduction
  • 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
  • Exercise: Optimize query performance

Implement CI/CD by using SQL Database Projects

  • 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

  • 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

Design and implement models and embeddings with SQL

  • 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

  • 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

  • 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

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