Data Private

Develop AI-enabled database solutions (DP-800T00)

3 days
Microsoft SQL ServerAIAzureMicrosoft FabricSQLDatabases

Design and develop AI-enabled database solutions across Microsoft SQL Server, Azure SQL, and Microsoft Fabric using vector search, embeddings, and more.

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Course Overview

Learn to design and develop AI-enabled database solutions across Microsoft SQL platforms, including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric.

Designed for data professionals, this course covers scalable solutions that combine structured and semi-structured data with AI capabilities such as models, embeddings, vector search, intelligent search, and retrieval-augmented generation (RAG).

Course Benefits

  • Design and implement database and programmability objects with SQL
  • Write advanced T-SQL for analytics, JSON processing, pattern matching, graph queries, and error handling
  • Use AI-assisted development tools with Microsoft SQL platforms
  • Implement database security, compliance, performance optimization, and monitoring
  • Build CI/CD workflows with SQL Database Projects
  • Integrate SQL solutions with Azure services, REST APIs, GraphQL, and event-driven patterns
  • Design and manage models and embeddings for SQL database workloads
  • Implement full-text, vector, hybrid, and ranked search solutions
  • Design retrieval-augmented generation solutions that use SQL database context

Delivery Methods

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Microsoft Certified Partner

Course Outline

  1. Design and implement database objects with SQL
    1. Understand your SQL Server-based platform choices
    2. Build effective tables
    3. Optimize with indexes
    4. Use specialized table types
    5. Enforce data integrity with constraints
    6. Manage JSON columns and indexes
    7. Partition tables for scale
    8. Module assessment
  2. Implement programmability objects with SQL
    1. Create views
    2. Create stored procedures
    3. Create scalar functions
    4. Create table-valued functions
    5. Create triggers
    6. Choose when to use each option
  3. Write advanced T-SQL code
    1. Organize queries with Common Table Expressions
    2. Apply window functions for analytics
    3. Process JSON data with built-in functions
    4. Match patterns with regular expressions
    5. Find approximate matches with fuzzy string functions
    6. Traverse relationships with graph queries
    7. Compare rows with correlated subqueries
    8. Handle errors with TRY...CATCH
    9. Module assessment
  4. Implement SQL solutions by using AI-assisted tools
    1. Describe AI-assisted development tools available for Microsoft SQL platforms
    2. Interpret the security impact of using AI-assisted tools
    3. Enable GitHub Copilot and Fabric Copilot
    4. Configure model and Model Context Protocol (MCP) tool options in a GitHub Copilot or Fabric Copilot chat session
    5. Create and configure GitHub Copilot instruction files
    6. Connect to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse
    7. Module assessment
  5. Implement data security and compliance with SQL
    1. Protect data with encryption
    2. Configure dynamic data masking
    3. Implement row-level security
    4. Manage permissions and secure access
    5. Implement auditing
    6. Configure secure access to AI services
    7. Secure data API endpoints
    8. Module assessment
  6. Optimize database performance
    1. Recommend database configurations
    2. Preserve data integrity with transaction isolation levels and concurrency controls
    3. Evaluate query performance with execution plans and DMVs
    4. Monitor and tune queries with Query Store and Query Performance Insight
    5. Identify and resolve blocking and deadlocks
  7. Implement CI/CD by using SQL Database Projects
    1. Create, build, and validate SQL Database Projects
    2. Configure source control and manage reference data
    3. Manage branching, pull requests, and conflict resolution
    4. Detect and resolve schema drift
    5. Implement CI/CD pipelines
    6. Design and implement a testing strategy
  8. Integrate SQL solutions with Azure services
    1. Create configuration files for Data API Builder
    2. Define entities for REST and GraphQL
    3. Expose database objects, stored procedures, and views
    4. Explore deployment options for Data API Builder
    5. Recommend Azure Monitor configurations
    6. Handle changes with event-driven patterns
    7. Module assessment
  9. Design and implement models and embeddings with SQL
    1. Understand and evaluate models for SQL database workloads
    2. Create and manage external models in SQL
    3. Design embeddings for SQL database workloads
    4. Generate and maintain embeddings for SQL database workloads
  10. Design and implement intelligent search with SQL
    1. Choose an intelligent search approach
    2. Implement full-text search
    3. Prepare SQL for vector search
    4. Implement vector search query patterns
    5. Implement hybrid search and ranking
  11. Design and implement RAG with SQL
    1. Identify RAG use cases and architecture
    2. Prepare retrieval context for augmentation
    3. Augment prompts with database context
    4. Generate and process RAG responses

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

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