​Let's Get Certified — DP-800: Microsoft Certified SQL AI Developer Associate Microsoft Student Ambassadors Community

​📅 Thursday, May 21, 2026 ⏰ 6:00 – 10:15 AM PT | 9:00 AM – 1:15 PM EDT | 2:00 – 6:15 PM WAT | 6:30 – 10:45 PM IST 📍 Virtual Event 💰 Free

​About This Event

​The DP-800: Microsoft Certified SQL AI Developer Associate certification validates the skills needed to design, secure, and implement AI capabilities across the Microsoft SQL ecosystem -- including SQL Server 2025, Azure SQL, and SQL Database in Microsoft Fabric.

​This community learning event focuses on the skills measured across the three DP-800 exam domains: design and develop database solutions (35-40%), secure, optimize, and deploy database solutions (35-40%), and implement AI capabilities in database solutions (25-30%).

​Each session gives you a focused building block for exam preparation. By the end of the event you will have a complete picture of how traditional database development merges with modern AI capabilities in the Microsoft SQL ecosystem.

​This event is part of the Microsoft Student Ambassador community learning series, designed to help students and early-career technologists prepare for Microsoft certifications while building real technical skills.

​🎟️ Exam Vouchers for Live Attendees

​Live attendees are eligible to request exam vouchers for the following certifications:

​DP-600: Microsoft Fabric Analytics Engineer Associate | DP-700: Microsoft Fabric Data Engineer Associate | DP-800: Microsoft Certified SQL AI Developer Associate

​Voucher details and the request link will be shared during the event. Rules for compliance apply. Vouchers are available while supplies last and are provided through the Microsoft Student Ambassador program.

​Who Should Attend

​Microsoft Student Ambassadors exploring AI-enabled database development

​Students preparing for the DP-800 certification

​SQL Server and Azure SQL developers expanding into AI application development

​Data engineers and analytics engineers working with Microsoft Fabric

​Cloud practitioners and developers building RAG and AI agent workflows

​Anyone interested in how SQL integrates with the modern AI application stack

​What You'll Learn

​How the DP-800 exam is structured across its three domains and what each one measures

​How the "One SQL" philosophy connects SQL Server 2025, Azure SQL, and Fabric

​How to design specialized tables, write advanced T-SQL, and work with the full range of JSON functions the exam tests

​How to secure, optimize, and deploy AI-enabled database solutions at production scale

​How vector search, embeddings, and DiskANN indexing work natively in SQL

​How to build RAG workflows that ground large language models in live database context

​How to expose database objects as REST and GraphQL endpoints using Data API Builder and MCP integration

​Agenda

​🟠 Opening Session - The SQL AI Developer Mindset and DP-800 Overview

​Teams: https://teams.microsoft.com/meet/231841632208643?p=gTgHUPlxzByLCQ98Jr

​Meeting ID: 231 841 632 208 643

​Passcode: 5XB7GT3M

​🕒 6:00 – 6:30 AM PT | 9:00 – 9:30 AM EDT | 2:00 – 2:30 PM WAT | 6:30 – 7:00 PM IST

​Speakers: Ladislau André, Philippa Burgess, Aaradhya Garg, Damir Krkalić, Midhuna Mohanraj

​This opening session orients attendees to the DP-800 exam structure, the Microsoft SQL AI ecosystem, and how each session in the event maps to the skills measured on the exam.

​Topics include:

​The "One SQL" architectural philosophy -- how SQL Server 2025, Azure SQL, and SQL Database in Microsoft Fabric form a unified platform for AI-enabled development

​Overview of the three DP-800 exam domains and their weighting

​How the event agenda maps to those domains session by session

​What to expect on the exam -- scenario-based, simulation-style, and multiple choice questions -- and what they are actually measuring

​Available preparation resources on Microsoft Learn and how to use them effectively

​🔵 Session 1 - Database Design, Advanced T-SQL, and JSON

​https://teams.microsoft.com/meet/297463818170503?p=NSnTIJXsyW2LQF6kVu

​Meeting ID: 297 463 818 170 503

​Passcode: px7tn3RU

​🕒 6:30 – 7:15 AM PT | 9:30 – 10:15 AM EDT | 2:30 – 3:15 PM WAT | 7:00 – 7:45 PM IST

​Speaker: Philippa Burgess

​This session covers the Design and Develop domain -- the largest portion of the DP-800 exam. It focuses on database object design, programmability, advanced T-SQL, and the JSON functions that appear heavily throughout the exam across multiple question types.

​Topics include:

​Specialized table types: Temporal, Ledger, Graph (MATCH operator), In-Memory, External, and JSON columns -- including when and why to use each

​JSON functions tested on the exam: JSON_VALUE, JSON_QUERY, JSON_CONTAINS, OPENJSON, JSON_OBJECT, JSON_ARRAY, JSON_MODIFY, JSON_ARRAYAGG, JSON_OBJECTAGG, ISJSON, JSON_PATH_EXISTS, FOR JSON PATH, FOR JSON AUTO, and WITHOUT_ARRAY_WRAPPER

​Regular expression functions: REGEXP_LIKE, REGEXP_REPLACE, REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_COUNT, REGEXP_MATCHES, and REGEXP_SPLIT_TO_TABLE

​Fuzzy string matching: EDIT_DISTANCE, EDIT_DISTANCE_SIMILARITY, JARO_WINKLER_DISTANCE, and JARO_WINKLER_SIMILARITY

​Programmability objects: views, scalar functions, table-valued functions, stored procedures, and triggers

​Configuring and using AI-assisted development tools including GitHub Copilot and Copilot in Fabric securely within a development workflow

​🟢 Session 2 - Securing, Optimizing, and Deploying Database Solutions

​https://teams.microsoft.com/meet/263873981099215?p=Ji6Mvl98rZwmMep5fS

​Meeting ID: 263 873 981 099 215

​Passcode: DG2FG7na

​🕒 7:15 – 8:00 AM PT | 10:15 – 11:00 AM EDT | 3:15 – 4:00 PM WAT | 7:45 – 8:30 PM IST

​Speaker: Midhuna Mohanraj

​This session covers the second major DP-800 exam domain -- securing, optimizing, and deploying database solutions. It focuses on data protection, performance tuning, CI/CD deployment patterns, and change tracking across the Microsoft SQL ecosystem.

​Topics include:

​Data security and compliance: Always Encrypted, Dynamic Data Masking, Row-Level Security, column-level encryption, object-level permissions, Managed Identities, passwordless authentication, and database auditing

​Performance tuning: reading Query Execution Plans, using Query Store, querying DMVs, and resolving common bottlenecks including parameter sniffing and missing indexes

​Columnstore indexes and table partitioning for analytical and large-scale workloads

​CI/CD for databases: SDK-style SQL Database Projects, schema drift management, secrets management, and state-based vs migration-based deployment strategies

​Change tracking patterns: Change Data Capture (CDC), Change Event Streaming (CES), and Azure Functions with SQL trigger bindings

​🟡 Session 3 - Vector Search, Embeddings, and Indexing

​https://teams.microsoft.com/meet/246955832296257?p=ojetpTi7pSCKIOZFUo

​Meeting ID: 246 955 832 296 257

​Passcode: VG7mE9nK

​🕒 8:00 – 8:45 AM PT | 11:00 – 11:45 AM EDT | 4:00 – 4:45 PM WAT | 8:30 – 9:15 PM IST

​Speaker: Aaradhya Garg

​This session covers the AI capabilities that are most technically distinct for candidates with a traditional SQL background -- how SQL Server 2025 handles vectors natively, how to generate and store embeddings, and how to build and query vector indexes.

​Topics include:

​The native vector data type and vector functions: VECTOR_NORM, VECTOR_NORMALIZE, and VECTOR_DISTANCE with cosine, euclidean, and dot product metrics

​CREATE EXTERNAL MODEL -- registering external AI models as named database objects for use in T-SQL queries

​AI_GENERATE_EMBEDDINGS -- calling a registered model to generate vector embeddings from text columns inline within a T-SQL query

​DiskANN vector indexes: how they work, the minimum 100-row requirement for index creation, and accuracy vs performance tradeoffs

​VECTOR_SEARCH: SELECT TOP (N) WITH APPROXIMATE syntax, iterative filtering with WHERE predicates, and full DML support for real-time inserts and updates on indexed tables

​🟠 Session 4 - Retrieval-Augmented Generation (RAG) and External REST Endpoints

​https://teams.microsoft.com/meet/29769334334668?p=WaRVMNFJOnrfqLA7qU

​Meeting ID: 297 693 343 346 68

​Passcode: qT7VB6rt

​🕒 8:45 – 9:30 AM PT | 11:45 AM – 12:30 PM EDT | 4:45 – 5:30 PM WAT | 9:15 – 10:00 PM IST

​Speaker: Damir Krkalić

​This session covers how the database connects to large language models as their grounding layer. It focuses on building the chunking, retrieval, formatting, and invocation steps of a complete RAG pipeline using T-SQL -- including where FOR JSON PATH and WITHOUT_ARRAY_WRAPPER appear in real AI application scenarios.

​Topics include:

​The complete database RAG workflow: chunking, embedding, storing, retrieving, formatting, and invoking -- end to end in T-SQL

​AI_GENERATE_CHUNKS -- splitting source text into chunks for embedding, including chunk size tradeoffs for retrieval accuracy

​Using FOR JSON PATH and WITHOUT_ARRAY_WRAPPER to shape retrieved chunks into the JSON request body format required by Azure OpenAI chat completions endpoints

​sp_invoke_external_rest_endpoint: authenticating with Managed Identities, the 150 concurrent connection cap, batching rows with FOR JSON to avoid per-row HTTPS overhead, and error handling for failed external calls

​Designing RAG pipelines that balance freshness, latency, and throughput at production scale

​🟣 Session 5 - Data API Builder, MCP Integration, and DP-800 Exam Strategy

​https://teams.microsoft.com/meet/27627587959142?p=GXsJJKHBHYaSzsoPkM

​Meeting ID: 276 275 879 591 42

​Passcode: GJ7sk3T3

​🕒 9:30 – 10:15 AM PT | 12:30 – 1:15 PM EDT | 5:30 – 6:15 PM WAT | 10:00 – 10:45 PM IST

​Speaker: Ladislau André

​The final session brings everything together with data exposure patterns, modern AI agent architecture, and practical strategies for approaching the DP-800 exam based on domain weighting and beta exam feedback.

​Topics include:

​Data API Builder: exposing tables, views, and stored procedures as REST or GraphQL endpoints via configuration files, including security, caching, pagination, and filtering

​MCP integration: how the Model Context Protocol acts as a standardized connection layer that allows AI agents to query live database context without custom integration code

​How Data API Builder and MCP complement each other in AI-enabled application architectures

​DP-800 exam strategy: how domain weighting translates to question distribution, how to approach scenario-based and simulation-style questions, and why JSON, security, and T-SQL carry more question volume than candidates typically expect

​Recommended study pacing, practice resources, and next steps after the exam