Microsoft Data & Power BI
DP-800T00: Develop AI-enabled database solutions
Develop AI-enabled database solutions with SQL.
4 daysIntermediate11 modulesDP-800T00
About this course
Develop AI-enabled database solutions with SQL. Over four days you will design database and programmability objects, write advanced T-SQL, and add vector search and AI capabilities to SQL solutions.
Curriculum
11 modules
- Understand your SQL Server-based platform choices
- Build effective tables
- Optimize with indexes
- Use specialized table types
- Enforce data integrity with constraints
- Manage JSON columns and indexes
- Partition tables for scale
- Module assessment
- Create views
- Create stored procedures
- Create scalar functions
- Create table-valued functions
- Create triggers
- Choose when to use each option
- Organize queries with Common Table Expressions
- Apply window functions for analytics
- Process JSON data with built-in functions
- Match patterns with regular expressions
- Find approximate matches with fuzzy string functions
- Traverse relationships with graph queries
- Compare rows with correlated subqueries
- Handle errors with TRY...CATCH
- Module assessment
- Describe AI-assisted development tools available for Microsoft SQL platforms
- Interpret security impact of using AI-assisted tools
- Enable GitHub Copilot and Fabric Copilot
- Configure model and Model Context Protocol (MCP) tool options in a GitHub Copilot or Fabric Copilot chat session
- Create and configure GitHub Copilot instruction files
- Connect to MCP server endpoints, including Microsoft SQL Server and Fabric Lakehouse
- Module assessment
- Protect data with encryption
- Configure dynamic data masking
- Implement row-level security
- Manage permissions and secure access
- Implement auditing
- Configure secure access to AI services
- Secure data API endpoints
- Module assessment
- Recommend database configurations
- Preserve data integrity with transaction isolation levels and concurrency controls
- Evaluate query performance with execution plans and DMVs
- Monitor and tune queries with Query Store and Query Performance Insight
- Identify and resolve blocking and deadlocks
- Create, build, and validate SQL Database Projects
- Configure source control and manage reference data
- Manage branching, pull requests, and conflict resolution
- Detect and resolve schema drift
- Implement CI/CD pipelines
- Design and implement a testing strategy
- Create configuration files for Data API Builder
- Define entities for REST and GraphQL
- Expose database objects, stored procedures, and views
- Explore deployment options for Data API Builder
- Recommend Azure Monitor configurations
- Handle changes with event-driven patterns
- Module assessment
- Understand and evaluate models for SQL database workloads
- Create and manage external models in SQL
- Design embeddings for SQL database workloads
- Generate and maintain embeddings for SQL database workloads
- Choose an intelligent search approach
- Implement full-text search
- Prepare SQL for vector search
- Implement vector search query patterns
- Implement hybrid search and ranking
- Identify RAG use cases and architecture
- Prepare retrieval context for augmentation
- Augment prompts with database context
- Generate and process RAG responses
Who it's for
Database developers who want to bring AI capabilities into their SQL solutions.
Before you start
No formal prerequisites. Bring curiosity and a willingness to practise.
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