Case Study 1 - Contoso Overview Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sal

Case Study 1 - Contoso Overview Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts. Existing Environment Identity Environment Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2. Data Environment Contoso has the following data environment:

• The Sales division uses a Microsoft Power BI Premium capacity.
• The semantic model of the Online Sales department includes a fact table named Orders that

uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.

• The Research department uses an on-premises, third-party data warehousing product.
• Fabric is enabled for contoso.com.
• An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division

data for a product line named Productline1. - The data is in the delta format.

• A Data Lake Storage Gen2 storage account named storage2 contains Research division data

for a product line named Productline2. The data is in the CSV format. Requirements Planned Changes Contoso plans to make the following changes:

• Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
• Make all the data for the Sales division and the Research division available in Fabric.
• For the Research division, create two Fabric workspaces named Productline1ws and

Productine2ws.

• In Productline1ws, create a lakehouse named Lakehouse1.
• In Lakehouse1, create a shortcut to storage1 named ResearchProduct.

Data Analytics Requirements Contoso identifies the following data analytics requirements:

• All the workspaces for the Sales division and the Research division must support all Fabric

experiences.

• The Research division workspaces must use a dedicated, on-demand capacity that has per-

minute billing.

• The Research division workspaces must be grouped together logically to support OneLake data

hub filtering based on the department name.

• For the Research division workspaces, the members of ResearchReviewersGroup1 must be

able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.

• For the Research division workspaces, the members of ResearchReviewersGroup2 must be

able to read lakehouse data by using Lakehouse explorer.

• All the semantic models and reports for the Research division must use version control that

supports branching. Data Preparation Requirements Contoso identifies the following data preparation requirements:

• The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric

notebooks.

• All the Research division data in the lakehouses must be presented as managed tables in

Lakehouse explorer. Semantic Model Requirements Contoso identifies the following requirements for implementing and managing semantic models:

• The number of rows added to the Orders table during refreshes must be minimized.
• The semantic models in the Research division workspaces must use Direct Lake mode.

General Requirements Contoso identifies the following high-level requirements that must be considered for all solutions:

• Follow the principle of least privilege when applicable.
• Minimize implementation and maintenance effort when possible.

What should you use to implement calculation groups for the Research division semantic models?