Move preview notice to end of introduction in Fabric IQ modules

Author: theresa-i

learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/includes/1-introduction.md

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 Imagine you work at Lamna Healthcare, a fictitious medical center that manages operations across hospitals, departments, and rooms.
 
-> [!IMPORTANT]
-> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).
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 Your data lives in multiple places—hospital, department, room, and patient records exist as lakehouse tables, while continuous vital signs monitoring from intensive care unit (ICU) equipment streams into an eventhouse. To enable natural language queries and exploration across all of it, you need to create an ontology that unifies these data sources with consistent business vocabulary.
 
 You face a key decision: how to start building your ontology. You can automatically generate structure from an existing Power BI semantic model, or build from scratch using OneLake data. Both paths lead to the same destination—a complete ontology bound to your lakehouse tables and eventhouse streams.
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 The ontology becomes your semantic layer—a unified graph model that AI agents and Graph in Microsoft Fabric use to answer questions. Instead of writing SQL joins between lakehouse and eventhouse, users explore connected business concepts through natural language or visual graph navigation.
 
 In this module, you build the complete Lamna Healthcare ontology by choosing your creation approach, modeling entity types and relationships, and binding them to OneLake data sources. By the end, you have a working ontology that connects Lamna Healthcare's patient records in the lakehouse to live vital signs monitoring in the eventhouse—ready for natural language queries and graph exploration.
+
+> [!IMPORTANT]
+> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).

learn-pr/wwl-data-ai/understand-fabric-iq-fundamentals/includes/1-introduction.md

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 Imagine you're a data analyst at Lamna Healthcare, responsible for helping clinical operations teams understand patient care patterns across your facility.
 
-> [!IMPORTANT]
-> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).
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 Patient records sit in lakehouse tables while vital signs stream continuously from ICU monitoring equipment into an eventhouse. When hospital administrators ask questions like "Which patients in the ICU have elevated vital signs?" or "How many beds are occupied on the surgical floor?", you need to manually join lakehouse tables with eventhouse streams, translate business terms into technical column names, and write complex queries.
 
 Business users can't explore the data themselves—they depend on you to write queries each time they have a question. By the time you deliver answers, clinical conditions may have already changed.
 
 Fabric IQ solves this challenge by letting you define business vocabulary in an ontology, then bind those concepts to your data sources in OneLake. You define concepts such as Patient, Department, and Room with their properties and relationships, creating a semantic layer. Business users can then ask questions in natural language through data agents or visually explore relationships through Graph in Microsoft Fabric—exploring the data themselves without needing you to write queries. 
 
 In this module, you'll discover what Fabric IQ is and how it works. You'll explore the components that work together—ontology items, data agents, Graph in Microsoft Fabric, and Power BI semantic models—and learn when to use each one. You'll also see how ontology modeling shifts your approach from use-case-driven thinking to concept-driven thinking, fundamentally changing how teams collaborate around data. 
+
+> [!IMPORTANT]
+> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).

learn-pr/wwl-data-ai/visualize-ontology-fabric-iq/includes/1-introduction.md

@@ -1,12 +1,12 @@
 Imagine you're a data analyst at Lamna Healthcare. You've built an ontology in Fabric IQ.
 
-> [!IMPORTANT]
-> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).
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 Hospital, Department, Room, Patient, and VitalSignEquipment entity types are defined, bound to the lakehouse and eventhouse, and structurally sound. Now comes the reason you built it.
 
 The clinical operations manager stops by with a question: "Which rooms in the Intensive Care Unit currently have patients, and which vital sign monitors are active there?" Before the ontology, answering that question meant writing a multi-table SQL join—linking patient assignment records to rooms, rooms to departments, and departments to equipment logs. With the ontology in place, you can explore the answer visually by following named relationships across your semantic layer.
 
 In this module, you open the entity type overview to inspect the entity instances that your data bindings have populated. You see individual Department, Room, and Patient records are drawn from your OneLake sources. You expand the relationship graph to visualize how those instances connect—patients assigned to rooms, rooms belonging to departments, equipment monitoring patients. You use the Query builder to add filters for specific property values, control which entity types and relationship types appear using the Components pane, and explore data that spans your lakehouse and eventhouse without writing any SQL.
 
 By the end of this module, you're equipped to turn the ontology into answers: exploring connected healthcare data the way business users think about it.
+
+> [!IMPORTANT]
+> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).