DP-800T00: Develop AI-enabled database solutions

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.

DP-800T00: Develop AI-enabled database solutions Course Content

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.