MDFT Pro, a well-known training agency, 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.
Identity Environment: MDFT Pro has a Microsoft Entra tenant named mdftpro.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.
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 mode. 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 mdftpro.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
Planned 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 Productline2ws
- In Productline1ws, create a lakehouse named Lakehouse1
- In Lakehouse1, create a shortcut to storage1 named ResearchProduct
Data Analytics Requirements:
- All 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, members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints
- For the Research division workspaces, members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer
- All semantic models and reports for the Research division must use version control that supports branching
Data Preparation Requirements:
- The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks
- All Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer
Semantic Model Requirements:
- 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:
- Follow the principle of least privilege when applicable
- Minimize implementation and maintenance effort when possible
You need to ensure that MDFT Pro can use version control to meet the data analytics requirements and the general requirements. What should you do?
Modify the settings of the Research workspaces to use a GitHub repository is incorrect. Fabric workspaces integrate with Azure Repos, not GitHub directly, for version control with branching support.
Store all the semantic models and reports in Microsoft OneDrive is incorrect. OneDrive doesn't provide version control capabilities with branching for Fabric semantic models and reports.
Store at the semantic models and reports in Data Lake Gen2 storage is incorrect. Data Lake Gen2 storage doesn't support version control with branching for semantic models and reports.
Modify the settings of the Research division workspaces to use an Azure Repos repository is correct. Azure Repos provides Git-based version control with branching support, meeting the Research division's requirement for managing semantic models and reports.