Merge pull request #54194 from sherzyang/main

Rewrite module.

Author: v-ccolin

learn-pr/wwl-data-ai/get-started-text-analysis-azure/1-introduction.yml

@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction"
-  ms.date: 03/24/2026
+  ms.date: 04/10/2026
   author: wwlpublish
   ms.author: sheryang
   ms.topic: unit

learn-pr/wwl-data-ai/get-started-text-analysis-azure/2-azure-language.yml

@@ -1,14 +1,14 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.get-started-with-text-analysis-in-azure.azure-language
-title: Azure Language
+title: Understand text analysis in Foundry
 metadata:
-  title: Azure Language
-  description: "Understand text analysis capabilities in Azure Language and the Foundry Language Playground."
-  ms.date: 03/24/2026
+  title: Understand text analysis in Foundry
+  description: "Understand text analysis capabilities with Foundry's general-purpose AI models and Azure Language in Foundry Tools."
+  ms.date: 04/10/2026
   author: wwlpublish
   ms.author: sheryang
   ms.topic: unit
   zone_pivot_groups: video-or-text
-durationInMinutes: 5
+durationInMinutes: 8
 content: |
   [!include[](includes/2-azure-language.md)]

learn-pr/wwl-data-ai/get-started-text-analysis-azure/3-language-sdk.yml

@@ -1,14 +1,14 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.get-started-with-text-analysis-in-azure.language-sdk
-title: Azure Language SDK
+title: Create a client application that analyzes text
 metadata:
-  title: Azure Language SDK
-  description: "Azure Language SDK capabilities"
-  ms.date: 03/24/2026
+  title: Create a client application that analyzes text
+  description: "Create a client application that analyzes text with both the OpenAI responses API and the Azure Language SDK."
+  ms.date: 04/10/2026
   author: wwlpublish
   ms.author: sheryang
   ms.topic: unit
   zone_pivot_groups: video-or-text
-durationInMinutes: 8
+durationInMinutes: 9
 content: |
   [!include[](includes/3-language-sdk.md)]

learn-pr/wwl-data-ai/get-started-text-analysis-azure/4-language-mcp.yml

@@ -4,7 +4,7 @@ title: Azure Language MCP
 metadata:
   title: Azure Language MCP
   description: "Learn how to use the Azure Language MCP to connect agents to text analysis capabilities."
-  ms.date: 03/24/2026
+  ms.date: 04/10/2026
   author: wwlpublish
   ms.author: sheryang
   ms.topic: unit

learn-pr/wwl-data-ai/get-started-text-analysis-azure/5-exercise.yml

@@ -4,7 +4,7 @@ title: Exercise - Get started with text analysis in Microsoft Foundry
 metadata:
   title: Exercise - Get started with text analysis in Microsoft Foundry
   description: "Exercise - Get started with text analysis in Microsoft Foundry"
-  ms.date: 02/10/2026
+  ms.date: 04/10/2026
   author: sherzyang
   ms.author: sheryang
   ms.topic: unit

learn-pr/wwl-data-ai/get-started-text-analysis-azure/6-knowledge-check.yml

@@ -4,36 +4,25 @@ title: Module assessment
 metadata:
   title: Module assessment
   description: "Knowledge check"
-  ms.date: 02/10/2026
+  ms.date: 04/10/2026
   author: wwlpublish
   ms.author: sheryang
   ms.topic: unit
-durationInMinutes: 4
+durationInMinutes: 3
 quiz:
   title: ""
   questions:
-  - content: "You want to use Azure Language to determine the key talking points in a text document. Which feature of the service should you use?"
+  - content: "You need to analyze text where the same input must return structured results based on statistical techniques. Which approach is most appropriate?"
     choices:
-    - content: "Sentiment analysis"
+    - content: "The OpenAI responses API, because it can follow natural language instructions."
       isCorrect: false
-      explanation: "Incorrect. Sentiment analysis returns a numeric score indicating how positive or negative the text is."
-    - content: "Key phrase extraction"
+      explanation: "Incorrect. The OpenAI responses API generates text probabilistically, so results can vary between calls — which is unsuitable for pipelines that require consistent output."
+    - content: "The Azure Language SDK, because it returns deterministic, structured output."
       isCorrect: true
-      explanation: "Correct. Key phrases can be used to identify the main talking points in a text document."
-    - content: "Entity detection"
+      explanation: "Correct. The Azure Language SDK uses purpose-built analyzers that return consistent, structured results for the same input, making it well-suited for automated pipelines."
+    - content: "The chat playground in Foundry, because it supports follow-up questions."
       isCorrect: false
-      explanation: "Incorrect. Entity detection identifies specific types of entity in the document, not the main talking points."
-  - content: "You use Azure Language to perform sentiment analysis on a sentence. The sentiment scores returned are: 0.04 positive, 0.36 neutral, and 0.60 negative. What do these scores indicate about the sentence sentiment?"
-    choices:
-    - content: "The document is positive."
-      isCorrect: false
-      explanation: "Incorrect. The sentiment is most likely the type with the highest sentiment score."
-    - content: "The document is neutral."
-      isCorrect: false
-      explanation: "Incorrect. The sentiment is most likely the type with the highest sentiment score."
-    - content: "The document is negative."
-      isCorrect: true
-      explanation: "Correct. The sentiment is most likely the type with the highest sentiment score, in this case 0.60 negative."
+      explanation: "Incorrect. The chat playground is an interactive exploration tool, not a programmatic solution for automated pipelines."
   - content: "What is the purpose of the client object in the Azure Language SDK?"
     choices:
     - content: "It stores the application's user interface settings."
@@ -56,3 +45,4 @@ quiz:
     - content: "To expose Azure Language capabilities to agents through the Model Context Protocol."
       isCorrect: true
       explanation: "Correct. The MCP server allows agents to access Azure Language capabilities in a structured way through the Model Context Protocol."
+ 

learn-pr/wwl-data-ai/get-started-text-analysis-azure/7-summary.yml

@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 03/24/2026
+  ms.date: 04/10/2026
   author: wwlpublish
   ms.author: sheryang
   ms.topic: unit

learn-pr/wwl-data-ai/get-started-text-analysis-azure/includes/1-introduction.md

@@ -1,6 +1,6 @@
 ::: zone pivot="video"
 
->[!VIDEO https://learn-video.azurefd.net/vod/player?id=d48bd645-61dd-47cb-9a17-9f7f093a528d]
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=31189f3e-0545-43f3-9362-a9936f904967]
 
 ::: zone-end
 
@@ -22,7 +22,7 @@ Consider some of these applications of text analysis:
 
 - **Legal Document Summarization**: Law firms manage lengthy case files, court rulings, and agreements. Text analysis techniques can help summarize complex legal texts, highlight important clauses, and classify documents by topic, enabling attorneys to work more efficiently and make informed decisions faster.
 
-Next, let's explore language capabilities on Azure. In particular, we’ll focus on **Azure language in Microsoft Foundry tools**, a pre-trained service that can handle multiple text analysis tasks. 
+Next, let's explore text analysis capabilities in Microsoft Foundry. 
 
 ::: zone-end