Design and implement database objects using SQL - Understand SQL Server–based platform options
- Create efficient tables
- Optimize using indexes
- Use specialized table types
- Ensure data integrity with constraints
- Manage JSON columns and their indexes
- Partition tables for scalability
- Exercise – Create and maintain database objects
Implement programmable objects using SQL - Create views
- Create stored procedures
- Create scalar functions
- Create table‑valued functions
- Create triggers
- Choose when to use each option
- Exercise – Implement programmable objects in SQL Server
Write advanced T‑SQL code - Organize queries using common table expressions (CTEs)
- Apply window functions for analytics
- Process JSON data using built‑in functions
- Perform pattern matching with regular expressions
- Find approximate matches using fuzzy‑matching functions
- Traverse relationships using graph queries
- Compare rows using correlated subqueries
- Handle errors using TRY…CATCH
- Exercise – Working with JSON functions
Implement SQL solutions using AI‑assisted tools - Describe AI‑assisted development tools available for Microsoft SQL platforms
- Interpret the security impact of using AI‑assisted tools
- Enable GitHub Copilot and Fabric Copilot
- Configure the model and Model Context Protocol (MCP) 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
- Exercise – Configure AI‑assisted tools for database development
Implement data security and compliance with SQL - Protect data using encryption
- Configure dynamic data masking
- Implement row‑level security (RLS)
- Manage permissions and secure access
- Implement auditing
- Configure secure access to AI services
- Secure data API endpoints
- Exercise – Implement security features
Optimize database performance - Recommend database configurations
- Preserve data integrity using transaction isolation levels and concurrency controls
- Evaluate query performance using execution plans and DMVs
- Monitor and optimize queries using Query Store and Query Performance Insight
- Identify and resolve blocking and deadlocks
- Exercise – Optimize query performance
Implement CI/CD using SQL Database projects - Create, build, and validate SQL Database projects
- Configure source control and manage reference data
- Manage branches, pull requests, and conflict resolution
- Detect and resolve schema drift
- Implement CI/CD pipelines
- Design and implement a testing strategy
- Exercise – Implement CI/CD using SQL Database projects
Integrate SQL solutions with Azure services - 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
- Manage changes using event‑driven models
- Exercise – Configure Data API Builder for a product catalog
Design and implement models and embeddings with SQL - Understand and evaluate models for SQL database workloads
- Create and manage external models in SQL
- Design embeddings for SQL workloads
- Generate and maintain embeddings for SQL workloads
- Exercise – Generate and update embeddings in Azure SQL Database
Design and implement intelligent search with SQL - Choose an intelligent search approach
- Implement full‑text search
- Prepare SQL for vector search
- Implement vector search query patterns
- Implement hybrid search and result ranking
- Exercise – Implement intelligent search using full‑text, vector, and hybrid queries
Design and implement RAG with SQL - Identify RAG use cases and architecture
- Prepare retrieval context for augmentation
- Enrich prompts with database context
- Generate and process RAG responses
- Exercise – Implement a RAG solution
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