Course
digicode: DP800
Develop AI-enabled Database Solutions – Intensive Training (DP-800)
DP-800
Course facts
Download as PDF- Designing specialized tables (e.g., temporal, graph, JSON) and implementing constraints, sequences, and partitioning
- Developing database logic using views, stored procedures, and scalar/table-valued functions to encapsulate business requirements
- Mastering advanced T-SQL with CTEs, window functions, JSON manipulation, graph querying, and structured error handling
- Implementing data security via Always Encrypted, Dynamic Data Masking, Row-Level Security, Microsoft Entra ID access, and auditing
- Optimizing performance by tuning configurations, choosing isolation levels, analyzing query plans, and resolving blocking/deadlocks
- Automating the database lifecycle with SQL Database Projects, source control, schema drift detection, and CI/CD pipelines (GitHub/Azure DevOps)
- Exposing database entities and logic securely using Data API Builder configurations for REST and GraphQL endpoints
- Integrating AI/ML by utilizing Copilot, managing external models, generating vector embeddings, and implementing hybrid search and RAG
1 Design and implement database objects with SQL
This module covers designing and implementing various database objects including tables with different data types, specialized table types, indexes, constraints, and partitioning strategies. You'll learn how to create and optimize database objects for modern SQL platforms.
2 Implement programmability objects with SQL
Learn how to create and use views, stored procedures, scalar functions, table-valued functions, and triggers to build maintainable, secure, and efficient database solutions.
3 Write advanced T-SQL code
Learn advanced T-SQL techniques including CTEs, window functions, JSON, regular expressions, fuzzy matching, graph queries, and error handling for SQL Server, Azure SQL, and Fabric.
4 Implement SQL solutions by using AI-assisted tools
Learn how to leverage GitHub Copilot and Fabric Copilot for AI-assisted database development across SQL Server, Azure SQL, and SQL databases in Microsoft Fabric.
5 Implement data security and compliance with SQL
Learn how to protect sensitive data and meet compliance requirements by implementing encryption, masking, access controls, and auditing across Microsoft's SQL platforms.
6 Optimize database performance
Optimize Azure SQL Database performance by choosing the right service tier and managing concurrency with transaction isolation levels. Analyze queries with execution plans and DMVs. Use Query Store for plan management and diagnose blocking and deadlocks.
7 Implement CI/CD by using SQL Database Projects
Implement CI/CD for SQL Database Projects with source control, branching, schema drift detection, automated pipelines, and testing strategies using GitHub Actions and Azure DevOps.
8 Integrate SQL solutions with Azure services
Create REST and GraphQL APIs for SQL databases using Data API Builder, deploy to Azure hosting services, and implement monitoring and event-driven change patterns.
9 Design and implement models and embeddings with SQL
Integrate AI models with Azure SQL Database using external models and built-in AI functions. Design effective embedding strategies and implement maintenance patterns to keep embeddings aligned with source data.
10 Design and implement intelligent search with SQL
Implement intelligent search capabilities in SQL Server and Azure SQL by combining traditional full-text search with semantic vector search. Establish a mental model for different search approaches, prepare SQL for vector-based search, and implement vector, hybrid, and ranking-based search patterns with performance considerations.
11 Design and implement RAG with SQL
This module teaches you how to implement Retrieval Augmented Generation (RAG) using Azure SQL Database. You learn to identify appropriate RAG scenarios, prepare SQL results as LLM context, construct augmented prompts, and process model responses.
This course is aimed at 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.
- Experience with SQL Server database development and T-SQL programming
- Understanding of basic database concepts like tables, joins, and transactions
- Familiarity with relational database design principles
- Familiarity with SQL Server Management Studio or Azure Data Studio
Prepare for the «Microsoft Certified: SQL AI Developer Associate (beta)» exam with this course.