Hey everyone! Microsoft just released the Day One Keynote Recording from FabCon Vienna! I used AI to get some useful timestamps/ chapters and summary notes: Chapter 1: Opening & Vision for Fabric 0:00 – 1:14 - Intro video with music and narration. - Themes: Every organization has data, but being AI-ready requires more than just data—it requires integration, governance, and scalability. - Welcome to FabCon 2025. - Introduction of Arun Ulag (Corporate VP, Azure Data). Chapter 2: Arun Ulag’s Welcome & Context 1:14 – 2:27 - Over 4,100 attendees: customers, partners, community members. - FabCon Europe now Microsoft’s largest European conference. - Hundreds of Microsoft engineers, architects, and designers present to engage directly. - Hundreds of customers and partners sharing their Fabric journeys. - Transition to Satya Nadella’s video message. Chapter 3: Satya Nadella’s Message 2:27 – 4:07 - Data is the foundation of AI. - Fabric = complete data and analytics platform: - Fastest-growing database product in Microsoft history. - 25,000+ customers (London Stock Exchange, Siemens, Lumen, etc.). - Microsoft itself runs on Fabric (“customer zero”). - Announcements: - Closing: “We’re just getting started.” Chapter 4: The Need for Data Unification 4:13 – 7:13 - AI is only as good as the data feeding it. - Traditional database categories (SQL, NoSQL, lakehouses, vector DBs, etc.) are blurring. - Customers don’t want to integrate 18 different products → demand for unification. - Fabric: - Microsoft leads across 4 Gartner Magic Quadrants (data integration, cloud DB, AI, BI). - Fabric launched 22 months ago → 25,000+ customers, 80% of Fortune 500. - 50% of customers use 3+ workloads (Data Factory, SQL/Spark, Power BI). Chapter 5: Customer Story – Siemens 8:07 – 12:59 Guest: Christian Meyer, Head of Platforms at Siemens - Challenge: fragmented tools → need harmonization. - Solution: Siemens “Data in the iCloud” = global data mesh. - Benefits: - Obstacles: - Future: scaling real-time intelligence, bringing AI closer to data with copilots/agents.

I have experimented with AI functions a lot recently and here are all my learnings so far on how to use these functions properly. I have written about the current best possible way to utilise AI functions in data engineering tasks along with classification and translation related tasks. Would love to know more interesting and correct ways you guys use Fabric AI functions or utilise AI in data ingestion tasks. https://uselessai.in/correct-way-to-use-ai-functions-on-your-data-microsoft-fabric-ai-functions-41788b4b4563?source=collection_home_page----1b5bd1c68ab5-----0-----------------------------------

This might be slightly off-topic, but just in case it is of interest as I was discussing this with Will in the comment section :). Took the AI900 this week. In my opinion this was easier than for example AZ900. I read through the MS Learn path once, took the assessment test and then went back to the learning resources it told me I should freshen up on. Took another practice assessment, watched about 75% of these videos: AI 900: https://www.youtube.com/watch?v=bTkUTkXrqOQ and https://www.youtube.com/watch?v=Ch6KE7KxHGM This was probably overkill as you can pass the exam solely with the MS Learn and a good understanding of when to use what Azure AI resource and responsible AI. Passed with 889🥳 Short notice on my background: I have worked with AI solutions and have a background in Comp/Theoretical Chemistry so knowing the mathematical model to choose for ML was already given, but the questions do not go into depth and beyond the basics this was of very little help. More might be tested more in the higher level certifications.

Has anyone in here done AI 900 and have study tips? What I would also find interesting is how close the practice assessment is to the actual exam, for AZ900 I thought the MS official assessment was a good guide, for DP6/700 it was more misleading than anything.

I passed DP-600 earlier today. Such a great feeling. Thanks, Will, and the entire community!