DP-800T00: Develop AI-enabled database solutions

This course equips data professionals with the knowledge and practical skills required to design, develop, secure, and optimise AI-enabled database solutions across Microsoft SQL platforms. Learners will explore modern database development techniques using SQL Server, Azure SQL Database, and SQL databases in Microsoft Fabric while integrating artificial intelligence capabilities into enterprise-grade applications.

Key focus areas include database design, advanced Transact-SQL development, AI-assisted development tools, security and compliance, performance optimisation, DevOps practices, intelligent search, vector embeddings, and retrieval-augmented generation (RAG). Through hands-on exercises and real-world scenarios, learners will gain the confidence to build scalable, secure, and intelligent data solutions that combine structured and semi-structured data with AI-powered capabilities.

Prerequisites

To get the most from this course, learners should have:

• Familiarity with Microsoft SQL platforms, including SQL Server, Azure SQL Database, or SQL databases in Microsoft Fabric
• Experience writing basic SQL queries and working with relational databases
• A foundational understanding of database design and development concepts
• Basic knowledge of data solution development and deployment

No prior experience with AI, vector search, embeddings, or retrieval-augmented generation (RAG) is required.

Target audience

This course is designed for professionals responsible for designing, developing, administering, or supporting data-driven solutions that incorporate AI capabilities.

Typical attendees include:

• SQL Developers
• Database Administrators
• Data Engineers
• Data Analysts
• Application Developers working with Microsoft SQL platforms
• Technical professionals responsible for modernising database solutions with AI-powered features

This course is ideal for organisations looking to develop applied skills in AI, Cloud, and Data technologies while building intelligent, scalable, and secure database applications.

By the end of this course, learners will be able to:

• Design and implement database objects across Microsoft SQL platforms using industry best practices
• Develop programmable database solutions using views, stored procedures, functions, and triggers
• Write advanced Transact-SQL code to support analytical, graph, JSON, and AI-enabled workloads
• Configure and use AI-assisted development tools, including GitHub Copilot and Fabric Copilot, to improve productivity
• Implement security, governance, and compliance controls for data and AI-enabled services
• Optimise database performance using monitoring tools, execution plans, transaction controls, and performance insights
• Implement source control, testing strategies, and continuous integration and continuous delivery pipelines using SQL Database projects
• Integrate SQL-based solutions with Azure services and modern application interfaces
• Design and manage vector embeddings and intelligent search capabilities within SQL platforms
• Build retrieval-augmented generation solutions that combine enterprise data with generative AI models

Design and implement database objects using SQL

Explore database design fundamentals and create scalable database structures across Microsoft SQL platforms.

Topics include:

• Understanding SQL Server-based platform options
• Creating efficient and maintainable tables
• Optimising database performance with indexes
• Using specialised table types
• Enforcing data integrity with constraints
• Managing JSON columns and associated indexes
• Partitioning tables to support scalability and performance

Hands-on exercise:

• Create and maintain database objects

Implement programmable objects using SQL

Learn how to create reusable database components that support application development and business logic.

Topics include:

• Creating views
• Creating stored procedures
• Creating scalar functions
• Creating table-valued functions
• Creating triggers
• Selecting the most appropriate programmable object for different scenarios

Hands-on exercise:

• Implement programmable objects in SQL Server

Write advanced T-SQL code

Develop advanced querying techniques that support analytical, semi-structured, and graph-based workloads.

Topics include:

• Organising queries using common table expressions
• Applying window functions for analytics
• Processing JSON data using built-in functions
• Performing pattern matching with regular expressions
• Finding approximate matches using fuzzy matching functions
• Traversing relationships with graph queries
• Comparing rows using correlated subqueries
• Implementing structured error handling using TRY…CATCH

Hands-on exercise:

• Working with JSON functions

Implement SQL solutions using AI-assisted tools

Explore how AI-assisted development tools can accelerate database development while maintaining governance and security.

Topics include:

• Understanding AI-assisted development tools for Microsoft SQL platforms
• Evaluating the security implications of AI-assisted development
• Enabling GitHub Copilot and Fabric Copilot
• Configuring model and Model Context Protocol options
• Creating and managing Copilot instruction files
• Connecting to Model Context Protocol server endpoints
• Working with Microsoft SQL Server and Fabric Lakehouse integrations

Hands-on exercise:

• Configure AI-assisted tools for database development

Implement data security and compliance with SQL

Protect sensitive data and ensure compliance across modern database solutions.

Topics include:

• Protecting data using encryption
• Configuring dynamic data masking
• Implementing row-level security
• Managing permissions and secure access
• Implementing auditing and monitoring controls
• Securing access to AI services
• Protecting data API endpoints

Hands-on exercise:

• Implement security features

Optimise database performance

Learn techniques for maintaining performance and reliability across production database environments.

Topics include:

• Recommending database configurations
• Managing transaction isolation levels and concurrency controls
• Evaluating query performance using execution plans and dynamic management views
• Monitoring performance using Query Store and Query Performance Insight
• Identifying and resolving blocking issues
• Troubleshooting deadlocks and contention

Hands-on exercise:

• Optimise query performance

Implement CI/CD using SQL Database projects

Apply modern DevOps practices to database development and deployment.

Topics include:

• Creating, building, and validating SQL Database projects
• Configuring source control repositories
• Managing reference data
• Working with branches and pull requests
• Resolving merge conflicts
• Detecting and addressing schema drift
• Implementing continuous integration and continuous delivery pipelines
• Designing database testing strategies

Hands-on exercise:

• Implement CI/CD using SQL Database projects

Integrate SQL solutions with Azure services

Extend database functionality by integrating SQL solutions with Azure-native services and APIs.

Topics include:

• Creating configuration files for Data API Builder
• Defining entities for REST and GraphQL endpoints
• Exposing database objects, stored procedures, and views
• Evaluating deployment options for Data API Builder
• Recommending Azure Monitor configurations
• Managing events using event-driven architectures

Hands-on exercise:

• Configure Data API Builder for a product catalog

Design and implement models and embeddings with SQL

Learn how AI models and vector embeddings can enhance modern database applications.

Topics include:

• Evaluating model options for SQL workloads
• Creating and managing external models
• Designing embeddings for database solutions
• Generating embeddings for AI-powered workloads
• Maintaining and updating embedding data

Hands-on exercise:

• Generate and update embeddings in Azure SQL Database

Design and implement intelligent search with SQL

Build intelligent search experiences using traditional and AI-powered search techniques.

Topics include:

• Selecting an intelligent search approach
• Implementing full-text search
• Preparing databases for vector search
• Designing vector search query patterns
• Implementing hybrid search strategies
• Optimising result ranking and relevance

Hands-on exercise:

• Implement intelligent search using full-text, vector, and hybrid queries

Design and implement RAG with SQL

Discover how retrieval-augmented generation solutions combine enterprise data with generative AI services.

Topics include:

• Identifying RAG use cases and architectural patterns
• Preparing retrieval context for augmentation
• Enriching prompts using database context
• Generating and processing RAG responses
• Implementing end-to-end AI-powered retrieval solutions

Hands-on exercise:

• Implement a RAG solution

Exams and assessments

Throughout the programme, learners will reinforce their understanding through practical exercises, guided labs, instructor-led discussions, and scenario-based activities aligned to real-world database development challenges.

The DP-800 certification exam is not included with this course. Learners who wish to pursue Microsoft certification should review the current certification requirements and exam availability separately.

Hands-on learning

This course includes extensive hands-on learning designed to help learners apply new skills immediately.

Participants will:

• Create and optimise database objects
• Develop programmable SQL solutions
• Configure AI-assisted development environments
• Implement security and compliance controls
• Build CI/CD pipelines for database deployments
• Generate embeddings and implement vector search
• Design and deploy retrieval-augmented generation solutions

Instructor-led exercises and practical labs provide opportunities to apply concepts within realistic enterprise scenarios.