Replaced image and added instructions for solutions

03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-01/solution-01.md

@@ -292,7 +292,7 @@ In this task, you will integrate operational data mirrored from Azure SQL Manage
 <tr><td colspan="2" align="center"><img src="../../Images/image029.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>In the <b>New shortcut</b> window, choose the source type and Under <b>External sources</b>, select <b>Azure Data Lake Storage Gen2</b> so you can directly link and access files stored in your organization's data lake, enabling seamless integration of external datasets with your Lakehouse environment for unified analytics.</td><td></td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image030.jpg" style="width: 100%; display: block;"></td></tr>
-<tr><td>Select <b>New connection</b> and Paste DataLake Storage URL: <code>https://adlsgen2employeedata.dfs.core.windows.net/</code> (1). Click <b>Next (2)</b> to continue.</td><td>This ensures you are establishing a secure and direct connection to your organization’s Data Lake for accessing external files.</td></tr>
+<tr><td>Select <b>New connection</b> and Paste DataLake Storage URL: <code>https://employeedata####.dfs.core.windows.net/</code> (1). Replace <code>####</code> in the URL with the current day and month in DDMM format (for example, <code>1206</code> for June 12). Click <b>Next (2)</b> to continue.</td><td>This ensures you are establishing a secure and direct connection to your organization’s Data Lake for accessing external files.</td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image031.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>Select the folder with <b><code>your user number</code> (1)</b>. Click <b>Next (2)</b> to continue.</td><td>Selecting your user-specific folder ensures you only access and work with the files intended for your lab activities.</td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image032.png" style="width: 100%; display: block;"></td></tr>

03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-02/solution-02.md

@@ -56,6 +56,8 @@ In this task, you will create a semantic model from the Lakehouse tables. A sema
 <tr><td colspan="2" align="center"><img src="../../Images/image041.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>Before clicking <b>Confirm</b>, make sure newly created tables (for example <b>employees</b>) is included in the selection.</td><td>Use your screenshot below to verify that the table is selected.</td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image151.png" style="width: 100%; display: block;"></td></tr>
+<tr><td>If you cannot see the <b>employees</b> table, switch to the SQL Analytics Endpoint view, expand <b>dbo</b>, and refresh the schema. Then return to the Lakehouse view and verify that the <b>employees</b> table is available.</td><td></td></tr>
+<tr><td colspan="2" align="center"><img src="../../Images/image042.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>Click on <b>Confirm</b>.</td><td></td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image152.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>Wait until the semantic model is successfully created. You will then see it listed in the workspace.</td><td>If creation fails, switch once to the SQL Analytics Endpoint view and create it from there.</td></tr>
@@ -300,6 +302,13 @@ In this step, open Power BI Copilot from the report and ask natural language que
 <tr><td colspan="2" align="center"><img src="../../Images/image154.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>Click <b>Copilot</b> in the report and select <b>Get started</b>.</td><td>You can now ask natural language questions directly against the prepared report and semantic model.</td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image048.png" style="width: 100%; display: block;"></td></tr>
+<tr><td colspan="2"><b>Try asking these sample questions:</b>
+<ul>
+<li>"What is this report about?"</li>
+<li>"Summarize the key insights from this report"</li>
+<li>"What are the most important trends here?"</li>
+</ul>
+</td></tr>
 </table>
 
 ## Summary

03-Azure/01-02 Data/03-Talk_to_your_data/walkthrough/challenge-03/solution-03.md

@@ -47,6 +47,7 @@ In this task, you will set up a Data Agent to enable intelligent data interactio
 <tr><td colspan="2" align="center"><img src="../../Images/image069.png" style="width: 100%; display: block;"></td></tr>
 <tr><td>In the Explorer pane on the left, confirm the Lakehouse is listed as an data source</td><td>Leave the Page open, you will work with this agent in the next chapter.</td></tr>
 <tr><td colspan="2" align="center"><img src="../../Images/image070.png" style="width: 100%; display: block;"></td></tr>
+<tr><td><b>Expand the Lakehouse node in the Explorer pane and verify that all required tables are visible and Ensure that all tables are checked and accessible. The Data Agent requires access to these tables to function properly.</td><td>If any tables are not visible, refresh the Lakehouse connection or check that the tables were successfully created in Challenge 1. Without proper table access, the Data Agent cannot answer questions accurately.</td></tr>
 </table>
 
 # 2. Prompt Engineering for Data Agents
@@ -113,6 +114,8 @@ You can also optionally compare your own setup with the reference instructions u
 
 💡 **Important:** AI-generated responses may differ each time, even for the same question.
 
+💡 **Tip:** If the Data Agent does not answer consistently or stops responding, clear the chat history first and try again with the same prompt.
+
 **Example Prompt:** 
 Identify the months with the highest toy sales. Then analyze whether those months also show higher customer satisfaction scores.