Develop AI-Enabled Database Solutions (DP-800)
Preparation for the "Azure Databricks Data Engineer Associate" Certification
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Machine Learning Engineer
1. Designing and Implementing Database Objects with SQL
This module covers the design and implementation of various database objects, including tables with different data types, special table types, indexes, constraints, and partitioning strategies. You will learn how to create and optimize database objects for modern SQL platforms.
2. Implementing Programming Objects with SQL
Learn how to create and use views, stored procedures, scalar functions, table-valued functions, and triggers to develop maintainable, secure, and efficient database solutions.
3. Writing 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. Implementing SQL Solutions Using AI-Powered Tools
Learn how to use GitHub Copilot and Fabric Copilot for AI-powered database development in SQL Server, Azure SQL, and SQL databases in Microsoft Fabric.
5. Implementing 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 all Microsoft SQL platforms.
6. Optimizing database performance
Optimize Azure SQL Database performance by selecting the appropriate service tier and managing concurrency with transaction isolation levels. Analyze queries using execution plans and DMVs. Use the Query Store for plan management and to diagnose deadlocks and blocking.
7. Implementing CI/CD for SQL Database Projects
Implement CI/CD for SQL Database projects with source code control, branching, schema change detection, automated pipelines, and testing strategies using GitHub Actions and Azure DevOps.
8. Integrating SQL solutions with Azure services
Create REST and GraphQL APIs for SQL databases using the Data API Builder, deploy them to Azure hosting services, and implement monitoring and event-driven change patterns.
9. Designing and Implementing Models and Embeddings with SQL
Integrate AI models into Azure SQL Database using external models and built-in AI features. Design effective embedding strategies and implement maintenance patterns to align the embeddings with the source data.
10. Designing and Implementing Intelligent Search with SQL
Implement intelligent search capabilities in SQL Server and Azure SQL by combining traditional full-text search with semantic vector search. Develop a mental model for various search approaches, prepare SQL queries for vector-based search, and implement vector-based, hybrid, and ranking-based search patterns while considering performance considerations.
11. Designing and Implementing RAG with SQL
In this module, you’ll learn how to implement Retrieval Augmented Generation (RAG) using Azure SQL Database. You’ll learn to identify suitable RAG scenarios, prepare SQL results as LLM context, create enhanced prompts, and process model responses.
Requirements:
- Experience in developing SQL Server databases and in T-SQL programming
- Understanding of basic database concepts such as tables, joins, and transactions
- Familiarity with the principles of relational database design
- Familiarity with SQL Server Management Studio or Azure Data Studio
- Designing specialized tables (e.g., time-based, graph, and JSON tables) and implementing constraints, sequences, and partitioning
- Developing database logic using views, stored procedures, and scalar/table-valued functions to encapsulate business requirements
- Master advanced T-SQL features such as CTEs, window functions, JSON manipulation, graph queries, and structured error handling
- Implementing data security using Always Encrypted, Dynamic Data Masking, Row-Level Security, Microsoft Entra ID access, and auditing
- Performance optimization through configuration adjustments, selection of isolation levels, analysis of query plans, and resolution of deadlocks
- Automating the database lifecycle with SQL Database Projects, source code management, schema drift detection, and CI/CD pipelines (GitHub/Azure DevOps)
- Securely expose database entities and logic using Data API Builder configurations for REST and GraphQL endpoints
- Integrating AI/ML through the use of Copilot, managing external models, generating vector embeddings, and implementing hybrid search and RAG
This course consists of training training and is led by a trainer who supervises the participants live. Theory and practice are taught with live demonstrations and practical exercises. The video conferencing software Zoom is used.
Prepare for the "Microsoft Certified: SQL AI Developer Associate (beta)" exam with this course.
This course is designed for data professionals who want to learn how to design and develop AI-powered database solutions on Microsoft’s SQL platforms, including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. In this role, you will develop database solutions that encompass both structured and semi-structured data and integrate AI capabilities into modern, highly scalable enterprise applications.
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