Updates to data modeling section

Day After Dashboard in a Day/DataModeling.md

@@ -61,7 +61,7 @@ We'll begin by enabling the setting to allow for editing data models in the clou
 
 <font size="6">✅ Lab check</font>
 
-With the SQL endpoint we are able to run ad-hoc SQL queries atop of our tables and visualize our results all within a browser session. In the next section we want to focus on modeling our data and preparing it for analysis.
+With the SQL endpoint we are able to run ad-hoc SQL queries atop of our tables and visualize our results using DirectQuery mode all within a browser session. In the next section we want to focus on modeling our data and preparing it for analysis.
 
 ### Create relationships
 
@@ -73,10 +73,13 @@ It is also recommend to strive to deliver the **right number of tables** with th
 
 ---
 
-1. From the ribbon, select **Reporting** and then **New Power BI dataset**.
+1. From the ribbon, select **Reporting** and then **New semantic model**.
 
     ![New Power BI dataset](./Media/new-powerbi-dataset.png)
 
+    > [!NOTE]
+    > The **New semantic model** option is also visible from the Lakehouse explorer's **Home** tab.
+
 1. In the **New dataset** window, update the **Name** to **SalesDirectLakeModel**. Seelct the following objects listed below and then click **Confirm**:
 
     | Object Name |
@@ -109,7 +112,9 @@ It is also recommend to strive to deliver the **right number of tables** with th
     > The **Assume referential integrity** selection, enables running more efficient queries by using INNER JOIN statements rather than OUTER JOIN. This feature is only available when using Direct Lake and DirectQuery connectivity modes.
     > Learn more about [Referential integrity](https://docs.microsoft.com/power-bi/connect-data/desktop-assume-referential-integrity)
 
-1. Perform these same steps for each of the remaining tables and columns listed in the following table to create relationships.
+1. From the **Home** tab, select the **Manage relationships** option, and for each of the items listed below select **New relationship** and configure the relationships for each of the remaining tables and columns. Once complete select **Close**.
+
+    ![Manage relationships](./Media/manage-relationships.png)
 
     | Make this relationship active | From: Table 1 (column) | To: Table 2 (column) | Cardinality | Assume referential integrity | Cross filter direction |
     | :----- |:----- | :------ | :----- | :----- | :----- |

Day After Dashboard in a Day/DataPreparation.md

@@ -3,7 +3,7 @@
 ✏️ Lab scenario
 ---
 
-In this section of the lab, our objective is to gather and merge daily files from a cloud directory. As the number of files in this directory will grow over time, it is essential to develop a data preparation solution that can accommodate this expansion. To achieve this, we need to create a future-proofed data preparation solution that can handle this growth1.
+In this section of the lab, our objective is to gather and merge daily files from a cloud directory. As the number of files in this directory will grow over time, it is essential to develop a data preparation solution that can accommodate this expansion. To achieve this, we need to create a future-proofed data preparation solution that can handle this growth.
 
 Please note that the term *future-proofed* refers to a solution that is designed to remain effective and relevant as conditions change over time. For more information on future-proofing queries, you can refer to the [Power Query best practices](https://learn.microsoft.com/power-query/best-practices#future-proofing-queries) documentation.
 
@@ -30,7 +30,7 @@ We'll begin by navigating to a new, empty, or non-production workspace to check
 
 ## Lakehouse storage
 
-To begin, we will create a **Lakehouse**, which is a data architecture platform that enables the storage, management, and analysis of structured and unstructured data. This flexible and scalable solution allows organizations to handle large volumes of data using a variety of tools and frameworks to process and analyze that data.
+We'll start by creating a **Lakehouse**, which is a data architecture platform that enables the storage, management, and analysis of structured and unstructured data. This flexible and scalable solution allows organizations to handle large volumes of data using a variety of tools and frameworks to process and analyze that data.
 
 Learn more about [lakehouses in Microsoft Fabric](https://learn.microsoft.com/fabric/data-engineering/lakehouse-overview)