Updated pre-req and intro wording.

theresa-i · theresa-i · commit ccaa157dfcd2 · 2026-02-09T17:11:36.000-05:00
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
@@ -1,4 +1,4 @@
-Imagine you're a data analyst at Lamna Healthcare Medical Center, 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.
 
diff --git a/learn-pr/wwl-data-ai/understand-fabric-iq-fundamentals/index.yml b/learn-pr/wwl-data-ai/understand-fabric-iq-fundamentals/index.yml
@@ -19,8 +19,8 @@ abstract: |
   - Compare ontology modeling's concept-driven approach with traditional use-case-driven data modeling
 prerequisites: |
   Before starting this module, you should have:
-  - Ability to navigate the Microsoft Fabric portal
-  - Conceptual understanding of data concepts such as tables, columns, and relationships
+  - The ability to navigate the Microsoft Fabric portal
+  - A conceptual understanding of data concepts such as tables, columns, and relationships
   - General familiarity with business intelligence and analytics scenarios
 iconUrl: /learn/achievements/generic-badge.svg
 levels:

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`@@ -1,4 +1,4 @@`
`1`		-Imagine you're a data analyst at Lamna Healthcare Medical Center, 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.
	`1`	+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.
`2`	`2`
`3`	`3`	`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.`
`4`	`4`