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?
A. INSERT INTO
B. CREATE TABLE AS SELECT (CTAS)
C. CREATE TABLE AS CLONE OF
D. CREATE MATERIALIZED VIEW AS SELECT
Show Answer
Correct Answer: B
Explanation: CREATE TABLE AS SELECT (CTAS) creates a new table directly from the results of a SELECT query, including joins across supported sources such as warehouse and lakehouse tables. It infers the resulting column data types from the query output, minimizing operations by combining table creation and data population into a single statement. INSERT INTO requires a preexisting table, CLONE only clones an existing table rather than a join result, and a materialized view creates a view rather than the requested point-in-time table.
Question 83
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?
A. spark.sql("SELECT * FROM Lakehouse1.Tables.ResearchProduct")
B. spark.read.format("delta").load("Tables/productline1/ResearchProduct")
C. external_table(ResearchProduct)
D. spark.read.format("delta").load("Tables/ResearchProduct")
Show Answer
Correct Answer: D
Explanation: The shortcut named ResearchProduct points to Delta-formatted data in ADLS Gen2. In a Fabric lakehouse, a shortcut to Delta data is exposed under the Tables area and can be read from a notebook using the Delta reader with the table path 'Tables/ResearchProduct'. The other options either use an invalid path, unsupported syntax, or an incorrect SQL reference.
Question 84
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?
A. Append variable
B. Lookup
C. Copy data
D. KQL
Show Answer
Correct Answer: B
Explanation: The Lookup activity can execute a query or stored procedure against a supported data source (including a Fabric warehouse) and return the result set as output. That output is available to downstream pipeline activities. Append variable only appends to an array variable, Copy data is for moving data rather than exposing scalar results for pipeline logic, and KQL is for querying Kusto data, not executing warehouse stored procedures.
Question 85
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?
A. Disable Query Caching for the default semantic model.
B. From the settings pane of LH1, enable Sync the default Power BI semantic model.
C. Enable Refresh for the default semantic model.
D. From the Endorsement and discovery settings of LH1, select Make discoverable.
Show Answer
Correct Answer: B
Explanation: To have new lakehouse tables automatically added to the default semantic model, enable the 'Sync the default Power BI semantic model' setting in the lakehouse settings. This synchronization keeps the default semantic model updated with newly created supported objects. Query caching, refresh, and discoverability do not control automatic inclusion of new tables.
Question 86
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?
A. UPDATE AND INSERT
B. MERGE
C. TRUNCATE TABLE and INSERT
D. CREATE TABLE AS SELECT
Show Answer
Correct Answer: A
Explanation: For the exam context, Fabric Warehouse did not support the MERGE T-SQL statement, so implementing a Type 2 SCD required separate UPDATE and INSERT statements. TRUNCATE/INSERT would remove history, and CTAS is intended for creating/loading tables rather than maintaining Type 2 history.
Sources:
https://learn.microsoft.com/en-us/fabric/data-factory/slowly-changing-dimension-type-two
Question 87
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?
A. Get metadata
B. Copy data
C. Lookup
D. Append variable
Show Answer
Correct Answer: C
Explanation: The Lookup activity can execute a query or stored procedure against a supported data source, including a Fabric warehouse, and returns the result as activity output. Those returned values are available to downstream pipeline activities via the activity output. Get metadata retrieves metadata only, Copy data is for data movement, and Append variable only appends values to an existing array variable rather than executing stored procedures.
Question 88
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?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: Yes. In Fabric/Power BI semantic models, USERPRINCIPALNAME() is the function intended to return the current user's UPN. If a measure using USERNAME() is not returning the desired value in this scenario, updating it to USERPRINCIPALNAME() meets the goal of returning the user's UPN.
Question 89
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?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The solution does not meet the goal. USEROBJECTID() returns the Microsoft Entra object ID of the current user, not the user's UPN. To return the user principal name in the Fabric service, the measure should use USERPRINCIPALNAME().
Question 90
You have a Microsoft Power BI Premium Per User (PPU) workspace that contains a semantic model.
You have an Azure App Service app named App1 that modifies row-level security (RLS) for the model by using the XMLA endpoint.
App1 requires users to sign in by using their Microsoft Entra credentials to access the XMLA endpoint.
You need to configure App1 to use a service account to access the model.
What should you do first?
A. Add a managed identity to the workspace.
B. Modify the XMLA Endpoint setting.
C. Upgrade the workspace to Premium capacity.
D. Add a managed identity to App1.
Show Answer
Correct Answer: C
Explanation: A Premium Per User (PPU) workspace supports XMLA endpoints but does not support app-only (service account/service principal) access to semantic models via the XMLA endpoint. In a PPU workspace, each user must authenticate interactively. To allow App1 to use a service account, the workspace must first be upgraded to a dedicated Premium (or equivalent supported capacity). After that, you can configure the app identity (such as a managed identity or service principal). The XMLA endpoint is already available, and managed identities are assigned to Azure resources like App Service, not Power BI workspaces.
Question 91
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.
Show Answer
Correct Answer: Yes
No
No
Explanation: CREATE TABLE ... AS CLONE OF in Microsoft Fabric creates a zero-copy clone by copying only table metadata. After creation, the clone is an independent table, so subsequent schema (DDL) or data (DML) changes to the source are not reflected in the clone.
$19
Get all 204 questions with detailed answers and explanations
Instant download HTML + PDF delivered the moment payment clears.
Secure Stripe checkout we never see or store your card details.
7-day refund if files are defective see our refund policy.
