Merge pull request #54295 from theresa-i/fabriciq-updates

AnnaMHuff · web-flow · commit 33176d4e4950 · 2026-04-20T08:53:44.000-07:00
Add Fabric IQ preview notices and remove (preview) from titles
diff --git a/learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/includes/1-introduction.md b/learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/includes/1-introduction.md
@@ -1,4 +1,6 @@
-Imagine you work at Lamna Healthcare, a fictitious medical center that manages operations across hospitals, departments, and rooms. 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.
+Imagine you work at Lamna Healthcare, a fictitious medical center that manages operations across hospitals, departments, and rooms.
+
+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.
 
@@ -7,3 +9,6 @@ Creating an ontology means defining business entity types like Hospital, Departm
 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).
diff --git a/learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/index.yml b/learn-pr/wwl-data-ai/create-ontology-with-fabric-iq/index.yml
@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.create-ontology-with-fabric-iq
 metadata:
-  title: Create an ontology (preview) with Fabric IQ
+  title: Create an Ontology with Fabric IQ
   description: Learn how to create ontologies in Fabric IQ by building manually or generating from Power BI semantic models, then bind ontology definitions to lakehouse tables and eventhouse streams.
   ms.date: 02/27/2026
   author: theresa-i
   ms.author: theresai
   ms.topic: module
   ms.service: fabric
   ai-usage: ai-generated
-title: Create an ontology (preview) with Fabric IQ
+title: Create an ontology with Fabric IQ
 summary: Ontologies in Fabric IQ transform your data into a business vocabulary that everyone can understand. In this module, you'll learn two ways to create ontologies - building manually to understand the core components, or generating automatically from Power BI semantic models to accelerate development. You'll practice both approaches and learn how to connect your ontology to data sources in OneLake, including lakehouse tables and eventhouse streams.
 abstract: |
   By the end of this module, you'll be able to:
diff --git a/learn-pr/wwl-data-ai/understand-fabric-iq-fundamentals/includes/1-introduction.md b/learn-pr/wwl-data-ai/understand-fabric-iq-fundamentals/includes/1-introduction.md
@@ -1,7 +1,12 @@
-Imagine you're a data analyst at Lamna Healthcare, responsible for helping clinical operations teams understand patient care patterns across your facility. 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.
+Imagine you're a data analyst at Lamna Healthcare, responsible for helping clinical operations teams understand patient care patterns across your facility.
+
+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).
diff --git a/learn-pr/wwl-data-ai/visualize-ontology-fabric-iq/includes/1-introduction.md b/learn-pr/wwl-data-ai/visualize-ontology-fabric-iq/includes/1-introduction.md
@@ -1,7 +1,12 @@
-Imagine you're a data analyst at Lamna Healthcare. You've built an ontology in Fabric IQ. 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.
+Imagine you're a data analyst at Lamna Healthcare. You've built an ontology in Fabric IQ.
+
+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).
diff --git a/learn-pr/wwl-data-ai/visualize-ontology-fabric-iq/index.yml b/learn-pr/wwl-data-ai/visualize-ontology-fabric-iq/index.yml
@@ -1,15 +1,15 @@
 ### YamlMime:Module
 uid: learn.wwl.visualize-ontology-fabric-iq
 metadata:
-  title: Visualize Ontology Data with Microsoft Fabric IQ (preview)
+  title: Visualize Ontology Data with Microsoft Fabric IQ
   description: Learn how to explore entity instances, visualize entity connections in the relationship graph, and filter ontology data using the Query builder with filters and components in Microsoft Fabric IQ.
   ms.date: 04/13/2026
   author: theresa-i
   ms.author: theresai
   ms.topic: module
   ms.service: fabric
   ai-usage: ai-generated
-title: Visualize ontology data with Microsoft Fabric IQ (preview)
+title: Visualize ontology data with Microsoft Fabric IQ
 summary: Explore entity instances, visualize business concept connections in the relationship graph, and filter across multiple data sources using the Query builder in Microsoft Fabric IQ—without writing SQL joins.
 abstract: |
   By the end of this module, you'll be able to:
