Address
Level 8/25 Restwell Street, Bankstown NSW 2200, Australia
Business Hours
Monday to Friday: 10AM - 6PM
Weekend: CLOSED
DP-800T00: Develop AI-enabled database solutions
This intermediate course teaches developers how to design and build AI-enabled database solutions across SQL Server, Azure SQL and SQL databases in Microsoft Fabric. You’ll learn to create database objects and write advanced T-SQL, then secure, optimise and deploy solutions with CI/CD practices. It also covers adding AI capabilities such as embeddings, vector search and model integration directly into your databases. It prepares you for the Microsoft Certified: SQL AI Developer Associate (DP-800) exam.
Available in Instructor-Led or Virtual formats.
Duration
3 Days
Modules
11 Modules
Course type
Microsoft
Course Overview
The course equips professionals to design, develop, secure, optimize, and deploy modern SQL database solutions enhanced with AI. It covers database objects, programmability, advanced T-SQL, AI-assisted development, security, performance tuning, CI/CD, and Azure integration. Learners also explore embeddings, vector data structures, intelligent search, and retrieval-augmented generation. By combining core database engineering with cloud and AI capabilities, the program prepares participants to build scalable, maintainable, compliant, and production-ready data solutions for enterprise environments using automation, secure architecture, and efficient SQL practices.
Before You Begin
- SQL and Relational Database Fundamentals
- Database Development and Optimization Basics
- Cloud and AI Awareness
Key outcomes
- Create tables, views, indexes, constraints, stored procedures, functions, and advanced T-SQL queries.
- Generate, optimize, test, and refactor SQL code to improve productivity and code quality.
- Implement authentication, encryption, auditing, masking, indexing, and performance-tuning techniques.
- Build CI/CD pipelines and connect SQL databases with Azure services for scalable cloud solutions.
- Work with embeddings, vector data, intelligent search, and Retrieval-Augmented Generation (RAG) using SQL.
Tools you’ll use
SQL
T-SQL
AI-Assisted SQL Development Tools
SQL Database Projects
Azure Services
SQL Vector Functions
Embedding & Vector Data Technologies
LLM Prompting and RAG Techniques
Skills you’ll gain
- SQL Database Design
- Database Programmability
- Advanced T-SQL
- AI-Assisted SQL Development
- Database Security & Compliance
- SQL Performance Optimization
- CI/CD for Databases
- Azure Database Integration
- Embeddings & Vector Data
- Intelligent Search
- RAG with SQL
What You’ll Learn
11 modules
1Module 1: Design and implement database objects with SQL
1.1 Introduction
1.2 Understand your SQL server-based platform choices
1.3 Build effective tables
1.4 Optimize with indexes
1.5 Use specialized table types
1.6 Enforce data integrity with constraints
1.7 Manage JSON columns and indexes
1.8 Partition tables for scale
1.9 Exercise – Create and maintain database objects
1.10 Summary
1.2 Understand your SQL server-based platform choices
1.3 Build effective tables
1.4 Optimize with indexes
1.5 Use specialized table types
1.6 Enforce data integrity with constraints
1.7 Manage JSON columns and indexes
1.8 Partition tables for scale
1.9 Exercise – Create and maintain database objects
1.10 Summary
2Module 2: Implement programmability objects with SQL
2.1 Introduction
2.2 Create views
2.3 Create stored procedures
2.4 Create scalar functions
2.5 Create table-valued functions
2.6 Create triggers
2.7 Choose when to use each option
2.8 Exercise: Implement programmability objects in SQL Server
2.9 Summary
2.2 Create views
2.3 Create stored procedures
2.4 Create scalar functions
2.5 Create table-valued functions
2.6 Create triggers
2.7 Choose when to use each option
2.8 Exercise: Implement programmability objects in SQL Server
2.9 Summary
3Module 3: Write advanced T-SQL code
3.1 Introduction
3.2 Organize queries with common table expressions
3.3 Apply window functions for analytics
3.4 Process JSON data with built-in functions
3.5 Match patterns with regular expressions
3.6 Find approximate matches with fuzzy string functions
3.7 Traverse relationships with graph queries
3.8 Compare rows with correlated subqueries
3.9 Handle errors with TRY…CATCH
3.10 Exercise – Work with JSON functions
3.11 Summary
3.2 Organize queries with common table expressions
3.3 Apply window functions for analytics
3.4 Process JSON data with built-in functions
3.5 Match patterns with regular expressions
3.6 Find approximate matches with fuzzy string functions
3.7 Traverse relationships with graph queries
3.8 Compare rows with correlated subqueries
3.9 Handle errors with TRY…CATCH
3.10 Exercise – Work with JSON functions
3.11 Summary
4Module 4: Implement SQL solutions by using AI-assisted tools
4.1 Introduction
4.2 Describe AI-assisted development tools available for Microsoft SQL platforms
4.3 Interpret security impact of using AI-assisted tools
4.4 Enable GitHub Copilot and Fabric Copilot
4.5 Configure model and model context protocol (MCP) tool options in a GitHub Copilot or Fabric Copilot chat session
4.6 Create and configure GitHub Copilot instruction files
4.7 Connect to MCP server endpoints, including Microsoft SQL Server and Fabric lakehouse
4.8 Exercise – Configure AI-assisted tools for database development
4.9 Summary
4.2 Describe AI-assisted development tools available for Microsoft SQL platforms
4.3 Interpret security impact of using AI-assisted tools
4.4 Enable GitHub Copilot and Fabric Copilot
4.5 Configure model and model context protocol (MCP) tool options in a GitHub Copilot or Fabric Copilot chat session
4.6 Create and configure GitHub Copilot instruction files
4.7 Connect to MCP server endpoints, including Microsoft SQL Server and Fabric lakehouse
4.8 Exercise – Configure AI-assisted tools for database development
4.9 Summary
5Module 5: Implement data security and compliance with SQL
5.1 Introduction
5.2 Protect data with encryption
5.3 Configure dynamic data masking
5.4 Implement row-level security
5.5 Manage permissions and secure access
5.6 Implement auditing
5.7 Configure secure access to AI services
5.8 Secure data API endpoints
5.9 Exercise: Implement security features
5.10 Summary
5.2 Protect data with encryption
5.3 Configure dynamic data masking
5.4 Implement row-level security
5.5 Manage permissions and secure access
5.6 Implement auditing
5.7 Configure secure access to AI services
5.8 Secure data API endpoints
5.9 Exercise: Implement security features
5.10 Summary
6Module 6: Optimize database performance
6.1 Introduction
6.2 Recommend database configurations
6.3 Preserve data integrity with transaction isolation levels and concurrency controls
6.4 Evaluate query performance with execution plans and DMVs
6.5 Monitor and tune queries with query store and query performance insight
6.6 Identify and resolve blocking and deadlocks
6.7 Exercise: Optimize query performance
6.8 Summary
6.2 Recommend database configurations
6.3 Preserve data integrity with transaction isolation levels and concurrency controls
6.4 Evaluate query performance with execution plans and DMVs
6.5 Monitor and tune queries with query store and query performance insight
6.6 Identify and resolve blocking and deadlocks
6.7 Exercise: Optimize query performance
6.8 Summary
7Module 7: Implement CI/CD by using SQL database projects
7.1 Introduction
7.2 Create, build, and validate SQL database projects
7.3 Configure source control and manage reference data
7.4 Manage branching, pull requests, and conflict resolution
7.5 Detect and resolve schema drift
7.6 Implement CI/CD pipelines
7.7 Design and implement a testing strategy
7.8 Exercise: implement CI/CD by using SQL database projects
7.9 Summary
7.2 Create, build, and validate SQL database projects
7.3 Configure source control and manage reference data
7.4 Manage branching, pull requests, and conflict resolution
7.5 Detect and resolve schema drift
7.6 Implement CI/CD pipelines
7.7 Design and implement a testing strategy
7.8 Exercise: implement CI/CD by using SQL database projects
7.9 Summary
8Module 8: Integrate SQL solutions with Azure services
8.1 Introduction
8.2 Create configuration files for data API builder
8.3 Define entities for REST and GraphQL
8.4 Expose database objects, stored procedures, and views
8.5 Explore deployment options for data API builder
8.6 Recommend Azure monitor configurations
8.7 Handle changes with event-driven patterns
8.8 Exercise – Configure data API builder for a product catalog
8.9 Summary
8.2 Create configuration files for data API builder
8.3 Define entities for REST and GraphQL
8.4 Expose database objects, stored procedures, and views
8.5 Explore deployment options for data API builder
8.6 Recommend Azure monitor configurations
8.7 Handle changes with event-driven patterns
8.8 Exercise – Configure data API builder for a product catalog
8.9 Summary
9Module 9: Design and implement models and embeddings with SQL
9.1 Introduction
9.2 Understand and evaluate models for SQL database workloads
9.3 Create and manage external models in SQL
9.4 Design embeddings for SQL database workloads
9.5 Generate and maintain embeddings for SQL database workloads
9.6 Exercise – Generate and update embeddings in Azure SQL Database
9.7 Summary
9.2 Understand and evaluate models for SQL database workloads
9.3 Create and manage external models in SQL
9.4 Design embeddings for SQL database workloads
9.5 Generate and maintain embeddings for SQL database workloads
9.6 Exercise – Generate and update embeddings in Azure SQL Database
9.7 Summary
10Module 10: Design and implement intelligent search with SQL
10.1 Introduction
10.2 Choose an intelligent search approach
10.3 Implement full-text search
10.4 Prepare SQL for vector search
10.5 Implement vector search query patterns
10.6 Implement hybrid search and ranking
10.7 Exercise – Implement intelligent search with full-text, vector, and hybrid queries
10.8 Summary
10.2 Choose an intelligent search approach
10.3 Implement full-text search
10.4 Prepare SQL for vector search
10.5 Implement vector search query patterns
10.6 Implement hybrid search and ranking
10.7 Exercise – Implement intelligent search with full-text, vector, and hybrid queries
10.8 Summary
11Module 11: Design and implement RAG with SQL
11.1 Introduction
11.2 Identify RAG use cases and architecture
11.3 Prepare retrieval context for augmentation
11.4 Augment prompts with database context
11.5 Generate and process RAG responses
11.6 Exercise: Implement a RAG solution
11.7 Summary
11.2 Identify RAG use cases and architecture
11.3 Prepare retrieval context for augmentation
11.4 Augment prompts with database context
11.5 Generate and process RAG responses
11.6 Exercise: Implement a RAG solution
11.7 Summary
Certification & Credentials
Certificate track
3 Days
High-Quality Video Lessons
Digital courseware, e-books & audio access
Recorded sessions, accessible on tablet & phone
AI mentor support & module quizzes
Learning Resources
Videos & Engaging Visual Content
Podcasts & Audiobooks
Expert discussions and audio episodes covering key topics
E-Books
Downloadable guides
Module-Wise Quizzes
Interactive quizzes after each module to reinforce learning.
Additional Resources
Study materials, exam prep guides, sandbox labs, and community tutorials.
Frequently asked questions
Who is this course designed for?
This course is designed for professionals who want to build modern database solutions by combining SQL expertise with AI, cloud integration, security, and performance optimization skills. Learners should have prior knowledge of SQL, relational databases, and basic database development concepts.
What prerequisites are required before starting this course?
Learners should have experience with relational database concepts, including tables, relationships, constraints, joins, and SQL queries. Familiarity with Azure services, AI concepts such as embeddings and search, and database optimization basics is also recommended.
What key skills will I gain after completing this course?
You will learn to design SQL database solutions, develop advanced T-SQL code, implement security practices, optimize database performance, automate deployments through CI/CD, and build AI-enabled solutions using embeddings, intelligent search, and RAG.
Will this course cover AI integration with databases?
Yes. The course covers AI capabilities within SQL solutions, including designing embeddings and vector data structures, implementing intelligent search, and developing Retrieval-Augmented Generation (RAG) pipelines using SQL, search, embeddings, and LLM prompts.
How will this course help in real-world database projects?
The course focuses on enterprise-ready skills such as secure database design, performance tuning, Azure integration, automated CI/CD deployment, and building scalable AI-powered data solutions suitable for modern applications.
| Duration | 3 Days |
|---|---|
| Modules | 11 Modules |
| Course type | Microsoft |
| Category | Microsoft |
| Prerequisites | SQL and Relational Database Fundamentals, Database Development and Optimization Basics, Cloud and AI Awareness |
Be the first to review “DP-800T00: Develop AI-enabled database solutions”
Reviews
There are no reviews yet.