Drop your questions or join us on stage in the end much recommended!

1. As a student trying to break into data and AI, beyond the hands-on labs, what is the absolute first tool or component within Microsoft Fabric a beginner should focus on?

2. For professionals from a non-AI background, which specific Fabric skill is most valued by employers today?

3. How do you handle the initial challenges of transforming raw source data into the well-structured Delta tables required for Direct Lake and ML models?

4. When training models, what is the decision criteria for using Fabric Notebooks versus an Azure ML compute cluster?

5. In moving to Data Activator, how do you avoid replacing dashboard fatigue with alert rule fatigue when setting initial thresholds?

6. How do you socialize AI-driven insights with engineering teams to build trust and ensure they act on the recommendations?

7. How does Fabric's Purview integration work in practice for automatically scanning and classifying data from real-time sources like Eventstream?

8. For an early-stage startup, is there a quick win use case for AI-augmentation in Fabric that delivers disproportionate value?

9. What does the migration path look like for enterprises with existing investments in other clouds or data tools?

10. How can the AI capabilities in Fabric be leveraged to create entirely new customer experiences or product offerings?

11. What does the operating model for a proactive intelligence team look like? Should experts be embedded or centralized?

12. In a pay-as-you-go model, what are the primary cost drivers and strategies for keeping them predictable?

13. Could you give an example of a specific metric or process within Fabric to proactively detect a biased outcome in a personalization model?

I have tried my best to arrange some from different socials.

One more from Param G., Implementation tips and timelines (including change management) around Al Augmented Product and systems Management.

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