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HOTSPOT

You have a Fabric tenant that contains a warehouse named WH1.

You have source data in a CSV file that has the following fields:

• SalesTransactionID
• SaleDate
• CustomerCode
• CustomerName
• CustomerAddress
• ProductCode
• ProductName
• Quantity
• UnitPrice

You plan to implement a star schema for the tables in WH1. The dimension tables in WH1 will implement Type 2 slowly changing dimension (SCD) logic.

You need to design the tables that will be used for sales transaction analysis and load the source data.

Which type of target table should you specify for the CustomerName, CustomerCode, and SaleDate fields? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have a Fabric tenant.

You are creating a Fabric Data Factory pipeline.

You have a stored procedure that returns the number of active customers and their average sales for the current month.

You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.

Which type of activity should you add?

HOTSPOT -

You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows.

You run the following T-SQL statement.

CREATE TABLE test.FactSales AS CLONE OF dbo.FactSales;

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You have an Amazon Web Services (AWS) subscription that contains an Amazon Simple Storage Service (Amazon S3) bucket named bucket1.

You have a Fabric tenant that contains a lakehouse named LH1.

In LH1, you plan to create a OneLake shortcut to bucket1.

You need to configure authentication for the connection.

Which two values should you provide? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You have a Fabric tenant that contains a data warehouse.

You need to load rows into a large Type 2 slowly changing dimension (SCD). The solution must minimize resource usage.

Which T-SQL statement should you use?

Case study -

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.

To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 Productline2ws.
• 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 Productline2 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.

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

You have a Fabric tenant that contains a lakehouse named LH1.

You create new tables in LH1.

You need to ensure that the tables are added automatically to the default semantic model.

What should you do?

You have a Fabric tenant.

You are creating a Fabric Data Factory pipeline.

You have a stored procedure that returns the number of active customers and their average sales for the current month.

You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.

Which type of activity should you add?

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.

After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.

Your network contains an on-premises Active Directory Domain Services (AD DS) domain named contoso.com that syncs with a Microsoft Entra tenant by using Microsoft Entra Connect.

You have a Fabric tenant that contains a semantic model.

You enable dynamic row-level security (RLS) for the model and deploy the model to the Fabric service.

You query a measure that includes the USERNAME() function, and the query returns a blank result.

You need to ensure that the measure returns the user principal name (UPN) of a user.

Solution: You update the measure to use the USEROBJECTID() function.

Does this meet the goal?

You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:
Table1: A Delta table created by using a shortcut
Table2: An external table created by using Spark

Table3: A managed table -
You plan to connect to Lakehouse1 by using its SQL endpoint.
What will you be able to do after connecting to Lakehouse1?

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.

After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.

Your network contains an on-premises Active Directory Domain Services (AD DS) domain named contoso.com that syncs with a Microsoft Entra tenant by using Microsoft Entra Connect.

You have a Fabric tenant that contains a semantic model.

You enable dynamic row-level security (RLS) for the model and deploy the model to the Fabric service.

You query a measure that includes the USERNAME() function, and the query returns a blank result.

You need to ensure that the measure returns the user principal name (UPN) of a user.

Solution: You update the measure to use the USERPRINCIPALNAME() function.

Does this meet the goal?

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.

After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.

Your network contains an on-premises Active Directory Domain Services (AD DS) domain named contoso.com that syncs with a Microsoft Entra tenant by using Microsoft Entra Connect.

You have a Fabric tenant that contains a semantic model.

You enable dynamic row-level security (RLS) for the model and deploy the model to the Fabric service.

You query a measure that includes the USERNAME() function, and the query returns a blank result.

You need to ensure that the measure returns the user principal name (UPN) of a user.

Solution: You add user objects to the list of synced objects in Microsoft Entra Connect.

Does this meet the goal?

HOTSPOT

You have a Fabric tenant that contains a PySpark notebook named Notebook1.
You define sas_token as a variable in the first cell of Notebook1 and store a shared access signature (SAS) token in the variable.
In the second cell, you run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You have a Fabric tenant that contains a warehouse named DW1 and a lakehouse named LH1. DW1 contains a table named Sales.Product. LH1 contains a table named Sales.Orders.

You plan to schedule an automated process that will create a new point-in-time (PIT) table named Sales.ProductOrder in DW1. Sales.ProductOrder will be built by using the results of a query that will join Sales.Product and Sales.Orders.

You need to ensure that the types of columns in Sales.ProductOrder match the column types in the source tables. The solution must minimize the number of operations required to create the new table.

Which operation should you use?

HOTSPOT

You have a Fabric tenant that contains the semantic model shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.

NOTE: Each correct selection is worth one point.