Prepare DP-600 Exam with Using DP-600 Dump Questions

1. You have a Fabric tenant that contains JSON files in OneLake. The files have one billion items.

You plan to perform time series analysis of the items.

You need to transform the data, visualize the data to find insights, perform anomaly detection, and share the insights with other business users.

The solution must meet the following requirements:

- Use parallel processing.

- Minimize the duplication of data.

- Minimize how long it takes to load the data.

What should you use to transform and visualize the data?

2. You have a Fabric workspace named Workspace1 and an Azure SQL database.

You plan to create a dataflow that will read data from the database, and then transform the data by performing an inner join.

You need to ignore spaces in the values when performing the inner join. The solution must minimize development effort.

What should you do?

3. HOTSPOT

You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.





You need to write a T-SQL query that will return the following columns.





How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.

4. HOTSPOT

You have a Fabric tenant that contains a workspace named Workspace_DEV. Workspace_DEV contains the semantic models shown in the following table.





Workspace_DEV contains the dataflows shown in the following table.





You create a new workspace named Workspace_TEST.

You create a deployment pipeline named Pipeline1 to move items from Workspace_DEV to Workspace_TEST.

You run Pipeline1.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

5. You have a Fabric workspace named Workspace1.

You need to create a semantic model named Model1 and publish Model1 to Workspace1.

The solution must meet the following requirements:

- Can revert to previous versions of Model1 as required.

- Identifies differences between saved versions of Model1.

- Uses Microsoft Power Bl=I Desktop to publish to Workspace1.

- Can edit item definition files by using Microsoft Visual Studio Code.

Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

6. 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?

7. You have a Fabric tenant that contains a warehouse.

You are designing a star schema model that will contain a customer dimension. The customer dimension table will be a Type 2 slowly changing dimension (SCD).

You need to recommend which columns to add to the table. The columns must NOT already exist in the source.

Which three types of columns should you recommend? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

8. You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 uses Pro license mode and contains a semantic model named Model1.

You need to ensure that Model1 supports XMLA connections.

Which setting should modify?

9. DRAG DROP

You have a Fabric workspace that contains a Dataflow Gen2 query. The query returns the following data.





You need to filter the results to ensure that only the latest version of each customer’s record is retained.

The solution must ensure that no new columns are loaded to the semantic model.

Which four actions should you perform in sequence in Power Query Editor? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

10. You have a Fabric tenant that contains a semantic model. The model contains 15 tables.

You need to programmatically change each column that ends in the word Key to meet the following requirements:

- Hide the column.

- Set Nullable to False

- Set Summarize By to None.

- Set Available in MDX to False.

- Mark the column as a key column.

What should you use?

11. HOTSPOT

You have a KQL database that contains a table named Readings.

You need to query Readings and return the results shown in the following table.





How should you complete the query? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

12. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.

When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.

You need to identify whether maintenance tasks were performed on Customer.

Solution: You run the following Spark SQL statement:

DESCRIBE DETAIL customer

Does this meet the goal?

13. You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake. The data contains details about customer demographics and product usage.

You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age, and customer tenure.

Which type of analytics are you performing?

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

15. You have a Fabric tenant that contains a semantic model.

You need to modify object-level security (OLS) for the model.

What should you use?

16. You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.

In Workspace1, you create a data pipeline named Pipeline1.

You have CSV files stored in an Azure Storage account.

You need to add an activity to Pipeline1 that will copy data from the CSV files to Lakehouse1. The activity must support Power Query M formula language expressions.

Which type of activity should you add?

17. Implement and manage semantic models



Testlet 1

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

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.



Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.



Available Data

Litware has data that must be analyzed as shown in the following table.





The Product data contains a single table and the following columns.





The customer satisfaction data contains the following tables:

- Survey

- Question

- Response

For each survey submitted, the following occurs:

- One row is added to the Survey table.

- One row is added to the Response table for each question in the survey.

The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.



User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.



Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.



Requirements

Planned Changes

Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Litware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity.



The following three workspaces will be created:

- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store

- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake

- DataSciPOC: Will contain all the notebooks and reports created by the data scientists



The following will be created in the AnalyticsPOC workspace:

- A data store (type to be decided)

- A custom semantic model

- A default semantic model

- Interactive reports



The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.



All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.



Technical Requirements

The data store must support the following:

- Read access by using T-SQL or Python

- Semi-structured and unstructured data

- Row-level security (RLS) for users executing T-SQL queries



Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.

Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model.

The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.

The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.

The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SQL queries and in the default semantic model.

The following logic must be used:

- List prices that are less than or equal to 50 are in the low pricing group.

- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.

- List prices that are greater than 1,000 are in the high pricing group.



Security Requirements

Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.

Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:

- Fabric administrators will be the workspace administrators.

- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.

- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.

- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.

- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.

- The date dimension must be available to all users of the data store.

- The principle of least privilege must be followed.



Both the default and custom semantic models must include only tables or views from the dimensional model in the data store.

Litware already has the following Microsoft Entra security groups:

- FabricAdmins: Fabric administrators

- AnalyticsTeam: All the members of the analytics team

- DataAnalysts: The data analysts on the analytics team

- DataScientists: The data scientists on the analytics team

- DataEngineers: The data engineers on the analytics team

- AnalyticsEngineers: The analytics engineers on the analytics team



Report Requirements

The data analysts must create a customer satisfaction report that meets the following requirements:

- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.

- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date.

- Shows data as soon as the data is updated in the data store.

- Ensures that the report and the semantic model only contain data from the current and previous year.

- Ensures that the report respects any table-level security specified in the source data store.

- Minimizes the execution time of report queries.



HOTSPOT

You need to create a DAX measure to calculate the average overall satisfaction score.

How should you complete the DAX code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

18. 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?

19. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a new semantic model in OneLake.

You use a Fabric notebook to read the data into a Spark DataFrame.

You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.

Solution: You use the following PySpark expression:

df.summary()

Does this meet the goal?

20. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.

When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.

You need to identify whether maintenance tasks were performed on Customer.

Solution: You run the following Spark SQL statement:

REFRESH TABLE customer

Does this meet the goal?

21. Question Set 3



You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 is assigned to a Fabric capacity.

You need to recommend a solution to provide users with the ability to create and publish custom Direct Lake semantic models by using external tools. The solution must follow the principle of least privilege.

Which three actions in the Fabric Admin portal should you include in the recommendation? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

22. You have a Fabric tenant that contains the workspaces shown in the following table.





You have a deployment pipeline named Pipeline1 that deploys items from Workspace_DEV to Workspace_TEST. In Pipeline1, all items that have matching names are paired.

You deploy the contents of Workspace_DEV to Workspace_TEST by using Pipeline1.

What will the contents of Workspace_TEST be once the deployment is complete?

23. 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.

24. You have a Fabric tenant that contains a workspace named Workspace1 and a user named User1. User1 is assigned the Contributor role for Workspace1.

You plan to configure Workspace1 to use an Azure DevOps repository for version control.

You need to ensure that User1 can commit items to the repository.

Which two settings should you enable for User1? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

25. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.

When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.

You need to identify whether maintenance tasks were performed on Customer.

Solution: You run the following Spark SQL statement:

DESCRIBE HISTORY customer

Does this meet the goal?

26. You have a Fabric tenant that contains a workspace named Workspace1.

You plan to deploy a semantic model named Model1 by using the XMLA endpoint.

You need to optimize the deployment of Model1. The solution must minimize how long it takes to deploy Model1.

What should you do in Workspace1?

27. HOTSPOT

You have a Fabric warehouse that contains the following data.





The data has the following characteristics:

- Each customer is assigned a unique CustomerID value.

- Each customer is associated to a single SalesRegion value.

- Each customer is associated to a single CustomerAddress value.

- The Customer table contains 5 million rows.

- All foreign key values are non-null.

You need to create a view to denormalize the data into a customer dimension that contains one row per distinct CustomerID value. The solution must minimize query processing time and resources.

How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

28. You have a Microsoft Power BI semantic model that contains a measure named TotalSalesAmount.

TotalSalesAmount returns a sales revenue amount that is translated into a selected currency.

You need to ensure that the value returned by TotalSalesAmount is formatted to use the correct currency symbol.

What should you include in the solution?

29. HOTSPOT

You are creating a report and a semantic model in Microsoft Power BI Desktop.

The Value measure has the expression 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.

30. You are creating a semantic model in Microsoft Power BI Desktop.

You plan to make bulk changes to the model by using the Tabular Model Definition Language (TMDL) extension for Microsoft Visual Studio Code.

You need to save the semantic model to a file.

Which file format should you use?

31. You have a Microsoft Power BI semantic model that contains measures. The measures use multiple CALCULATE functions and a FILTER function.

You are evaluating the performance of the measures.

In which use case will replacing the FILTER function with the KEEPFILTERS function reduce execution time?

32. You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.

What should you do?

33. HOTSPOT

You have two Microsoft Power BI queries named Employee and Retired Roles.

You need to merge the Employee query with the Retired Roles query. The solution must ensure that duplicate rows in each query are removed.

Which column and Join Kind should you use in Power Query Editor? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.

34. 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?

35. 1.HOTSPOT

You have a Fabric tenant.

You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power BI visuals.

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.

36. DRAG DROP

You have a Fabric tenant that contains a data warehouse named DW1. DW1 contains a table named DimCustomer. DimCustomer contains the fields shown in the following table.





You need to identify duplicate email addresses in DimCustomer. The solution must return a maximum of 1,000 records.

Which four T-SQL statements should you run in sequence? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.

37. You have a Fabric tenant that contains a semantic model.

You need to prevent report creators from populating visuals by using implicit measures.

What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

38. HOTSPOT

You have a Fabric tenant that contains lakehouse named Lakehouse1. Lakehouse1 contains a Delta table with eight columns.

You receive new data that contains the same eight columns and two additional columns.

You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data.

You need to add the new data to the Delta table to meet the following requirements:

- Keep all the existing rows.

- Ensure that all the new data is added to the table.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

39. HOTSPOT

You have a Fabric tenant that contains a lakehouse named Lakehouse1.

Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following table:





You create a Fabric notebook and attach it to Lakehouse1.

You need to use PySpark code to transform the data.

The solution must meet the following requirements:

- Add a column named pickupDate that will contain only the date portion of pickupDateTime.

- Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

40. 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?

41. Implement and manage semantic models



Testlet 2

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 Productline1 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.



What should you use to implement calculation groups for the Research division semantic models?

42. You have a Fabric tenant that contains a lakehouse.

You plan to query sales data files by using the SQL endpoint. The files will be in an Amazon Simple Storage Service (Amazon S3) storage bucket.

You need to recommend which file format to use and where to create a shortcut.

Which two actions should you include in the recommendation? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

43. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a new semantic model in OneLake.

You use a Fabric notebook to read the data into a Spark DataFrame.

You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.

Solution: You use the following PySpark expression:

df.explain()

Does this meet the goal?

44. Maintain a data analytics solution



Testlet 2



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

Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.



Existing Environment

Fabric Environment

Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.



Available Data

Litware has data that must be analyzed as shown in the following table.





The Product data contains a single table and the following columns.





The customer satisfaction data contains the following tables:

- Survey

- Question

- Response



For each survey submitted, the following occurs:

- One row is added to the Survey table.

- One row is added to the Response table for each question in the survey.



The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.



User Problems

The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.



Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.



Requirements

Planned Changes

Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Litware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity.



The following three workspaces will be created:

- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store

- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake

- DataSciPOC: Will contain all the notebooks and reports created by the data scientists



The following will be created in the AnalyticsPOC workspace:

- A data store (type to be decided)

- A custom semantic model

- A default semantic model

- Interactive reports



The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.



All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.



Technical Requirements

The data store must support the following:

- Read access by using T-SQL or Python

- Semi-structured and unstructured data

- Row-level security (RLS) for users executing T-SQL queries



Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.



Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model.



The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.



The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.



The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SQL queries and in the default semantic model.

The following logic must be used:

- List prices that are less than or equal to 50 are in the low pricing group.

- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.

- List prices that are greater than 1,000 are in the high pricing group.



Security Requirements

Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.



Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:

- Fabric administrators will be the workspace administrators.

- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.

- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.

- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook

- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.

- The date dimension must be available to all users of the data store.

- The principle of least privilege must be followed.



Both the default and custom semantic models must include only tables or views from the dimensional model in the data store.

Litware already has the following Microsoft Entra security groups:

- FabricAdmins: Fabric administrators

- AnalyticsTeam: All the members of the analytics team

- DataAnalysts: The data analysts on the analytics team

- DataScientists: The data scientists on the analytics team

- DataEngineers: The data engineers on the analytics team

- AnalyticsEngineers: The analytics engineers on the analytics team



Report Requirements

The data analysts must create a customer satisfaction report that meets the following requirements:

- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.

- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date.

- Shows data as soon as the data is updated in the data store.

- Ensures that the report and the semantic model only contain data from the current and previous year.

- Ensures that the report respects any table-level security specified in the source data store.

- Minimizes the execution time of report queries.



HOTSPOT

You need to assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.

Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

45. HOTSPOT

You need to migrate the Research division data for Productline1. The solution must meet the data preparation requirements.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

46. You have a Fabric workspace named Workspace1 that contains a dataflow named Dataflow1. Dataflow1 has a query that returns 2,000 rows.

You view the query in Power Query as shown in the following exhibit.





What can you identify about the pickupLongitude column?

47. Which type of data store should you recommend in the AnalyticsPOC workspace?

48. You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.

You perform the following actions:

- Create a workspace named Workspace2.

- Create a deployment pipeline named DeployPipeline1 that will deploy items from Workspace1 to Workspace2.

- Add a folder named Folder1 to Workspace1.

- Move Lakehouse1 to Folder1.

- Run DeployPipeline1.

Which structure will Workspace2 have when DeployPipeline1 is complete?

49. Your company has a finance department.

You have a Fabric tenant, an Azure Storage account named storage1, and a Microsoft Entra group named Group1. Group1 contains the users in the finance department.

You need to create a new workspace named Workspace1 in the tenant.

The solution must meet the following requirements:

- Ensure that the finance department users can create and edit items in Workspace1.

- Ensure that Workspace1 can securely access storage1 to read and write data.

- Ensure that you are the only admin of Workspace1.

- Minimize administrative effort.

You create Workspace1.

Which two actions should you perform next? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

50. You have a Fabric tenant that contains a lakehouse named Lakehouse1.

You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.

What should you configure?

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

52. HOTSPOT

You have a Fabric tenant that contains three users named User1, User2, and User3. The tenant contains a security group named Group1. User1 and User3 are members of Group1.

The tenant contains the workspaces shown in the following table.





The tenant contains the domains shown in the following table.





User1 creates a new workspace named Workspace3.

You assign Domain1 as the default domain of Group1.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

53. HOTSPOT

You have a Fabric tenant that contains a lakehouse.

You are using a Fabric notebook to save a large DataFrame by using the following code.

df.write.partitionBy(“year”, “month”, “day”).mode(“overwrite”).parquet(“Files/ SalesOrder”)

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:

54. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a Fabric tenant that contains a new semantic model in OneLake.

You use a Fabric notebook to read the data into a Spark DataFrame.

You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.

Solution: You use the following PySpark expression:

df.explain().show()

Does this meet the goal?

55. You have a Fabric tenant.

You plan to create a data pipeline named Pipeline1. Pipeline1 will include two activities that will execute in sequence.

You need to ensure that a failure of the first activity will NOT block the second activity.

Which conditional path should you configure between the first activity and the second activity?

56. You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse1 contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.

You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.

What should you use?

57. You have a Fabric tenant that contains a Microsoft Power BI report named Report1. Report1 includes a Python visual.

Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed.

You need all rows to appear in the visual.

What should you do?

58. 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?

59. HOTSPOT

You have a Microsoft Power BI report and a semantic model that uses Direct Lake mode.

From Power BI Desktop, you open Performance analyzer as 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.

60. DRAG DROP

You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.

You need to write a DAX query that will be executed by using the XMLA endpoint. The query must return a table of stores that have opened since December 1, 2023.

How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

Select and Place: