HOTSPOT
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You have a Microsoft Power Bl project that contains a file named definition.pbir. definition.pbir contains the following JSON.
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: No
No
Yes
Explanation: The JSON shows version 1.0, which is not the PBIR‑Legacy format.
The semantic model is referenced by a local path (byPath: ../Sales.Dataset), not the Power BI service.
Because the model is referenced locally, Power BI Desktop opens the semantic model in full edit mode.
Question 62
You have a Fabric warehouse that contains a table named SalesOrderDetail, SalesOrderDetail contains three columns named OrderQty, ProductID and SalesOrderlD. SalesOrderDetail contains one row per combination of SalesOrderlD and ProductID.
You need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order.
Which T-SQL statement should you run?
A.
B.
C.
D.
Show Answer
Correct Answer: D
Explanation: To calculate the proportion of each product’s quantity within a sales order, you must divide OrderQty by the total OrderQty for that same SalesOrderID. This requires a window aggregate using SUM(OrderQty) OVER (PARTITION BY SalesOrderID). Option D correctly uses PARTITION BY SalesOrderID, ensuring the total is calculated per sales order rather than across all rows or merely ordered rows.
Question 63
You have a Microsoft Power BI project that contains a semantic model.
You plan to use Azure DevOps for version control.
You need to modify the .gitignore file to prevent the data values from the data sources from being pushed to the repository.
Which file should you reference?
A. unappliedChanges.json
B. cache.abf
C. localSettings.json
D. model.bim
Show Answer
Correct Answer: B
Explanation: In a Power BI Project (.pbip), the file that actually contains cached data values from the data sources is cache.abf. This binary file stores the local data cache of the semantic model and can include large volumes of data. Adding cache.abf to .gitignore prevents source data values from being committed to Azure DevOps. The other files contain metadata or local configuration, not the cached dataset itself.
Question 64
You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales.
You need to visualize a diagram of the model. The diagram must contain only the Sales table and related tables.
What should you use from Microsoft Power BI Desktop?
A. data categories
B. Data view
C. Model view
D. DAX query view
Show Answer
Correct Answer: C
Explanation: Power BI Desktop’s Model view provides a visual diagram of tables and their relationships. It supports creating focused diagrams that include only a subset of tables, such as the Sales fact table and its related dimension tables, making it the correct choice for visualizing a star schema segment.
Question 65
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 create a role in the model.
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: Creating a role in the semantic model does not change the value returned by the USERNAME() function. In Fabric/Power BI, USERNAME() can return a blank when using Microsoft Entra ID–based authentication and dynamic RLS. To return the user principal name (UPN), a different function such as USERPRINCIPALNAME() must be used. Therefore, creating a role alone does not meet the goal.
Question 67
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Table1. Table1 contains the following data.
You need to perform the following actions:
• Load the data from Table1 into a star schema.
• Create a product dimension table named DimProduct and a fact table named FactSales.
Which three columns should you include in DimProduct?
A. ProductColor, ProductID, and ProductName.
B. ProductName, SalesAmount, and TransactionlD.
C. Date, ProductID, and TransactionlD.
D. ProductID, ProductName, and SalesAmount
Show Answer
Correct Answer: A
Explanation: In a star schema, a dimension table contains descriptive attributes about an entity, while measures and transactional data belong in fact tables. DimProduct should therefore include stable, descriptive product attributes such as ProductID (the business or surrogate key), ProductName, and ProductColor. Columns like SalesAmount, TransactionID, or Date are measures or transactional/time-related fields and belong in fact or date tables, not in a product dimension.
Question 68
You have a Fabric warehouse named Warehouse1 that contains a table named Table1. Table1 contains customer data.
You need to implement row-level security (RLS) for Table1. The solution must ensure that users can see only their respective data.
Which two objects should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. DATABASE ROLE
B. STORED PROCEDURE
C. CONSTRAINT
D. FUNCTION
E. SECURITY POLICY
Show Answer
Correct Answer: D, E
Explanation: In Microsoft Fabric Warehouse, row-level security is implemented by defining a security predicate in a user-defined function and then applying that predicate to a table through a security policy. The FUNCTION contains the row-filtering logic, and the SECURITY POLICY enforces that logic on Table1. Database roles may be used optionally for user management, but they are not required objects to implement RLS itself.
Question 69
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: The requirement is to create a new physical table in the warehouse from the result of a query that joins two source tables, while automatically preserving matching column data types and minimizing steps. CREATE TABLE AS SELECT (CTAS) creates the table and populates it in a single operation, deriving column definitions directly from the query result. INSERT INTO would require a pre-created table, CLONE can only duplicate an existing table (not a join result), and a materialized view is not a table and does not meet the PIT table requirement.
Question 70
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 ResearchProduct shortcut in Lakehouse1 points to Delta-formatted data in OneLake. In Fabric notebooks, lakehouse tables and shortcuts are accessed through the lakehouse filesystem, where shortcuts appear under the Tables directory. Using spark.read.format("delta").load("Tables/ResearchProduct") correctly reads the Delta data via the shortcut with minimal effort and proper Fabric support. The other options either reference an invalid path, use unsupported syntax, or do not align with how lakehouse shortcuts are accessed in notebooks.
Question 71
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: In Fabric Data Factory pipelines, a Lookup activity can execute a SQL query or stored procedure against a warehouse and return scalar values or a result set. The output of the Lookup activity is available to downstream activities via dynamic content, which satisfies the requirement to use the stored procedure’s returned values later in the pipeline. Append variable does not execute database logic, Copy data is for data movement rather than capturing scalar outputs, and KQL is not applicable for executing warehouse stored procedures.
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