DP-800T00: Develop AI-enabled database solutions

Štruktúra kurzu

Táto časť nie je lokalizovaná

Module 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 

Module 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 

Module 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 

Module 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 

Module 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 

Module 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 

Module 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 

Module 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 

Module 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 

Module 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 

Module 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