• Design and develop database solutions across Microsoft SQL platforms.
• Implement database objects and programmability features.
• Write advanced T-SQL code.
• Use AI-assisted development tools for SQL solutions.
• Implement database security and compliance.
• Optimize database performance.
• Implement CI/CD using SQL Database Projects.
• Integrate SQL solutions with Azure services.
• Implement models and embeddings.
• Implement full-text, vector, and hybrid search.
• Design and implement retrieval-augmented generation solutions.

Design and develop database solutions

• Design and implement database objects
• Implement programmability objects
• Write advanced T-SQL code
• Design and implement SQL solutions by using AI-assisted tools

Secure, optimize, and deploy database solutions

• Implement data security and compliance
• Optimize database performance
• Implement CI/CD by using SQL Database Projects
• Integrate SQL solutions with Azure services

Implement AI capabilities in database solutions

• Design and implement models and embeddings
• Design and implement intelligent search
• Design and implement retrieval-augmented generation (RAG)

Design and develop database solutions

• Design and implement tables, indexes, specialized tables, constraints, sequences, and partitioning.
• Create views, functions, stored procedures, and triggers.
• Write CTEs, window-function queries, JSON queries, regular-expression queries, fuzzy string matching queries, graph queries, and correlated queries.
• Implement error handling.
• Use GitHub Copilot and Microsoft Copilot in Fabric.
• Configure AI-assisted development tools and Model Context Protocol (MCP) connections.

Secure, optimize, and deploy database solutions

• Implement encryption, Dynamic Data Masking, Row-Level Security, permissions, passwordless access, and auditing.
• Secure model, REST, GraphQL, and MCP endpoints.
• Evaluate query performance using execution plans, DMVs, Query Store, and Query Performance Insight.
• Troubleshoot blocking and deadlocks.
• Build, test, validate, version, and deploy SQL Database Projects.
• Implement branching, pull requests, secrets management, schema-drift detection, and deployment controls.
• Configure Data API Builder and REST/GraphQL endpoints.
• Configure Azure Monitor, Application Insights, and Log Analytics.
• Implement change event streaming, change data capture, Change Tracking, Azure Functions SQL trigger bindings, and Azure Logic Apps.

Implement AI capabilities in database solutions

• Evaluate and manage external AI models.
• Design and generate embeddings.
• Select columns and chunk data for embeddings.
• Implement full-text, semantic vector, and hybrid search.
• Work with vector data types, vector indexes, and vector search functions.
• Select between approximate nearest neighbor and exact nearest neighbor search.
• Implement reciprocal rank fusion.
• Evaluate vector and hybrid search performance.
• Identify RAG use cases.
• Use sp_invoke_external_rest_endpoint to work with language models.
• Convert structured data to JSON for language-model processing.
• Send data to language models and process their responses.

3 Days

Certification: Microsoft Certified: SQL AI Developer Associate
Exam: DP-800: Developing AI-enabled database solutions
Duration: 100 minutes
Format: Computer-based, proctored Microsoft role-based certification examination

• Database developers.
• Data professionals working with Microsoft SQL Server, Azure SQL, or SQL databases in Microsoft Fabric.
• Professionals developing AI-enabled database solutions.
• Application developers working with SQL-based data services.
• Professionals working with vector search, embeddings, and AI-enabled database applications.

• Experience writing T-SQL code.
• Experience developing databases on Microsoft SQL platforms.
• Familiarity with CI/CD practices in GitHub.
• Familiarity with AI-assisted development tools.
• Understanding of AI concepts such as embeddings, vectors, and models.