Contoso, Ltd. is a health supplements company based in the US, and it is structured into two divisions: Sales and Research. The Sales division consists of two departments, namely Online Sales and Retail Sales, while the Research division assigns internally developed product lines to individual teams of researchers and analysts. Contoso utilizes a Microsoft Entra tenant named contoso.com, which includes two groups: ResearchReviewersGroup1 and ResearchReviewersGroup2. The Sales division leverages Microsoft Power BI Premium capacity. The semantic model within the Online Sales department features a fact table named Orders, operating in Import mode. Within the system of origin, the OrderID value signifies the sequence in which orders are created. The Research division relies on an on-premises, third-party data warehousing solution. Fabric is enabled for the contoso.com tenant. An Azure Data Lake Storage Gen2 account named storage1 stores data for a product line called Productline1 within the Research division, formatted in the delta format. Another Data Lake Storage Gen2 account named storage2 holds data for a product line called Productline2, presented in CSV format.

Contoso plans the following changes:

• Enable Fabric support in the Power BI Premium capacity for the Sales division.
• Make all data from the Sales and Research divisions available in Fabric.
• Create two Fabric workspaces for the Research division named Productline1ws and Productline2ws.
• In Productline1ws, create a lakehouse named Lakehouse1 and create a shortcut to storage1 named ResearchProduct.

Contoso has identified these data analytics requirements:

• All workspaces for both the Sales and Research divisions must support all Fabric experiences.
• Research division workspaces must utilize a dedicated, on-demand capacity with per-minute billing.
• Research division workspaces must be logically grouped to support OneLake data hub filtering by department name.
• ResearchReviewersGroup1 must have read access to lakehouse and warehouse data and shortcuts using SQL endpoints in the Research division workspaces.
• ResearchReviewersGroup2 must have read access to lakehouse data using Lakehouse explorer in the Research division workspaces.
• All semantic models and reports for the Research division must integrate version control with branching support.

Data preparation requirements identified by Contoso include:

• Retrieving Research division data for Productline1 from Lakehouse1 using Fabric notebooks.
• Presenting all Research division data in lakehouses as managed tables in Lakehouse explorer.

Requirements for implementing and managing semantic models include:

• Minimizing the number of rows added to the Orders table during refreshes.
• Utilizing Direct Lake mode for semantic models in the Research division workspaces.

High-level requirements identified by Contoso for all solutions include:

• Following the principle of least privilege wherever applicable.
• Minimizing implementation and maintenance efforts when possible.

Which syntax should you use in a notebook to access the Research division data for Productline1?