update exercise link

Author: madiepev

learn-pr/wwl-data-ai/optimize-finetune-agents/6-exercise.yml

@@ -1,9 +1,9 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.optimize-finetune-agents.exercise
-title: Exercise - Fine-tune a language model
+title: Exercise
 metadata:
-  title: Exercise - Fine-tune a language model
-  description: Exercise - Fine-tune a language model in the Microsoft Foundry portal.
+  title: Exercise 
+  description: Exercise 
   author: madiepev
   ms.author: madiepev
   ms.date: 02/25/2026

learn-pr/wwl-data-ai/optimize-finetune-agents/includes/6-exercise.md

@@ -1,9 +1,8 @@
-Now, it's your chance to explore how to fine-tune a foundation model from the model catalog using the Microsoft Foundry portal.
+Now, it's your chance to explore how to optimize fine-tuning a base model.
 
 > [!NOTE]
-> To complete this lab, you will need an [Azure subscription](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) in which you have administrative access.
+> This exercise uses a simulated notebook environment, so no Azure subscription or resources are required to complete it.
 
 Launch the exercise and follow the instructions.
 
-[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2277719&azure-portal=true)
-
+[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2354596&azure-portal=true)

learn-pr/wwl-data-ai/optimize-finetune-agents/index.yml

@@ -19,7 +19,6 @@ abstract: |
     - Evaluate and select fine-tuning methods including supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and direct preference optimization (DPO) based on quality requirements, data availability, and cost constraints.
     - Design and manage synthetic data strategies for fine-tuning, including assessing data quality requirements, generating diverse training examples, validating synthetic data effectiveness, and managing data versioning.
     - Assess and optimize fine-tuned model performance by interpreting evaluation metrics, identifying performance regressions, making data-driven optimization decisions, and validating improvements meet production thresholds.
-    - Architect model lifecycle workflows from development through production deployment, including model versioning strategies, testing approaches, deployment pipeline design, and rollback procedures.
 prerequisites: |
   Before starting this module, you should be familiar with fundamental AI concepts and services in Azure.
 iconUrl: /learn/achievements/generic-badge.svg