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- Design gold layers, semantic models, and documentation that enable Copilot, data agents, and enterprise ontologies to deliver accurate, business-relevant insights.
By the end of this module, you're able to:
- Explain how semantic model context guides Copilot answers.
- Design a focused gold layer for AI consumption.
- Select the appropriate Prep for AI control for a semantic model failure.
- Decide when to use Fabric IQ to share stable semantic model entities across teams.
- Validate AI readiness by testing priority questions and fixing the cause of wrong answers.
- Learn the fundamentals of Microsoft Fabric IQ, including how ontologies create a shared business vocabulary, the six items in the IQ workload, and how ontology modeling differs from traditional data modeling approaches.
By the end of this module, you'll be able to:
- Explain what Fabric IQ is and how it fits within the Microsoft IQ enterprise intelligence layer
- Describe the role of ontology items in creating entity types, entity instances, properties, relationships, and rules
- Distinguish between the roles of each IQ workload item: ontology items, data agents, Graph in Microsoft Fabric, semantic models, operations agents, and planning
- Compare ontology modeling's concept-driven approach with traditional use-case-driven data modeling
- Learn how to create ontologies in Fabric IQ by building manually or generating from Power BI semantic models, then bind ontology definitions to OneLake data sources including lakehouse tables and eventhouse streams.
By the end of this module, you'll be able to:
- Evaluate two approaches for creating an ontology in Fabric IQ
- Build an ontology manually by working with entity types, properties, keys, and relationships
- Generate an ontology from a Power BI semantic model
- Connect an ontology to data sources and preview the ontology
