Fix preview notice: move paragraph text out of blockquote

Author: theresa-i

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

@@ -1,7 +1,9 @@
 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). 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.
+> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).
+
+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.
 

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

@@ -1,7 +1,9 @@
 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). 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.
+> Fabric IQ is currently in [preview](/fabric/fundamentals/preview).
+
+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.