Let's Get Certified: DP-800 - Microsoft Certified SQL AI Developer Associate

​📅 Thursday, November 5, 2026
⏰ 6:30 to 10:45 AM ET | 7:30 to 11:45 PM PHT | 5:00 to 9:15 PM IST | 12:30 to 4:45 PM WAT
📍 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 presented by the Microsoft Fabric Location Intelligence & Security User Group as part of Fabric Data Days, and is designed to help students and early-career technologists prepare for Microsoft certifications and build real technical skills.

​🎤 Speakers

• ​Mou Rakshit, Avanade
  /mourakshit

• ​Ladislau André, Solution Architect
  /ladislau-andré-data-analyst

• ​Taha Mehjabin, Technology Researcher & Developer, Independent
  /taha-mehjabin-hi

• ​Philippa Burgess, Founder & Principal, GeoCyber Systems LLC
  /philippaburgess

• ​Mohammad Faizan, Microsoft Associate, Data Decoded
  /mohammad-faizan-5952b03bb

​💬 Moderators

• ​Nandan Hegde, Microsoft Data Platform MVP
  /nandan-hegde-4a195a66

• ​Teemu Multanen
  /teemu-multanen

• ​Lehlohonolo Mofula
  /lehlohonolom

​🎟️ Get Certified for Free with Data Days

​Data Days is offering free exam vouchers for:

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

​This is a Data Days event, so attending counts toward the session requirement for the voucher offer. See the full requirements, deadlines and request form here:
https://aka.ms/dd/cert

​Vouchers are limited and offered while supplies last, so request yours early.

​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
🎤 Philippa Burgess, with speakers and moderators
📺 YouTube Livestream
🕒 6:30 to 7:00 AM ET | 7:30 to 8:00 PM PHT | 5:00 to 5:30 PM IST | 12:30 to 1:00 PM WAT

​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
🎤 Mou Rakshit, Avanade
📺 YouTube Livestream
🕒 7:00 to 7:45 AM ET | 8:00 to 8:45 PM PHT | 5:30 to 6:15 PM IST | 1:00 to 1:45 PM WAT

​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 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

• ​Constraints and sequences: primary keys, foreign keys, UNIQUE, CHECK, DEFAULT and SEQUENCE

• ​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, triggers and error handling

• ​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
🎤 Ladislau André, Solution Architect
📺 YouTube Livestream
🕒 7:45 to 8:30 AM ET | 8:45 to 9:30 PM PHT | 6:15 to 7:00 PM IST | 1:45 to 2:30 PM WAT

​This session covers the second major DP-800 exam domain. 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, missing indexes, blocking and deadlocks

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

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

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

​🟡 Session 3: Vector Search, Embeddings and Indexing
🎤 Taha Mehjabin, Technology Researcher & Developer, Independent
📺 YouTube Livestream
🕒 8:30 to 9:15 AM ET | 9:30 to 10:15 PM PHT | 7:00 to 7:45 PM IST | 2:30 to 3:15 PM WAT

​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: approximate nearest neighbor queries, iterative filtering with WHERE predicates, and support for inserts and updates on indexed tables

• ​Hybrid search: combining full-text and vector results for meaning and exact keyword matches

​🟠 Session 4: Retrieval-Augmented Generation (RAG) and External REST Endpoints
🎤 Philippa Burgess, Founder & Principal, GeoCyber Systems LLC & Co-Lead Microsoft Fabric Location Intelligence & Security User Group
📺 YouTube Livestream
🕒 9:15 to 10:00 AM ET | 10:15 to 11:00 PM PHT | 7:45 to 8:30 PM IST | 3:15 to 4:00 PM WAT

​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 in 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 required by Azure OpenAI chat completions endpoints

• ​sp_invoke_external_rest_endpoint: authenticating with Managed Identities, concurrency limits, batching rows with FOR JSON to avoid per-row HTTPS overhead, and handling errors from 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
🎤 Mohammad Faizan, Microsoft Associate, Data Decoded
📺 YouTube Livestream
🕒 10:00 to 10:45 AM ET | 11:00 to 11:45 PM PHT | 8:30 to 9:15 PM IST | 4:00 to 4:45 PM WAT

​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 through configuration files, including security, caching, pagination and filtering

• ​MCP integration: how the Model Context Protocol acts as a standardized connection layer that lets AI agents work with 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