DP-600 Free Dumps (Part 2, Q41-Q70) V14.02 Released – 30 More Free Demo Questions for Exam Preparation

1. 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.

2. Prepare data



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

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.



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

3. You need to implement the date dimension in the data store. The solution must meet the technical requirements.

What are two ways to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

4. HOTSPOT

You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a warehouse named DW1. DW1 contains two tables named Employees and Sales. All users have read access to Dw1.

You need to implement access controls to meet the following requirements:

- For the Sales table, ensure that the users can see only the sales data from their respective region.

- For the Employees table, restrict access to all Personally Identifiable Information (PII).

- Maintain access to unrestricted data for all the users.

What should you use for each table? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

5. 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.

6. HOTSPOT

You have a Fabric tenant.

You need to configure OneLake security for users shown in the following table.


The solution must follow the principle of least privilege.

Which permission should you assign to each user? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

7. HOTSPOT

You have a Fabric tenant that contains a workspace named Enterprise. Enterprise contains a semantic model named Model1. Model1 contains a date parameter named Date1 that was created in Power Query.

You build a deployment pipeline named Enterprise Data that includes two stages named Development and Test. You assign the Enterprise workspace to the Development stage.

You need to perform the following actions:

- Create a workspace named Enterprise [Test] and assign the workspace to the Test stage.

- Configure a rule that will modify the value of Date1 when changes are deployed to the Test stage.

Which two settings should you use? To answer, select the appropriate settings in the answer area. NOTE: Each correct answer is worth one point.

8. You have a Fabric tenant that contains a data pipeline.

You need to ensure that the pipeline runs every four hours on Mondays and Fridays.

To what should you set Repeat for the schedule?

9. HOTSPOT

You need to recommend a solution to group the Research division workspaces.

What should you include in the recommendation? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

10. 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.

11. You have a Fabric tenant that contains a Microsoft Power BI report.

You are exploring a new semantic model.

You need to display the following column statistics:

- Count

- Average

- Null count

- Distinct count

- Standard deviation

Which Power Query function should you run?

12. DRAG DROP

You are implementing two dimension tables named Customers and Products in a Fabric warehouse.

You need to use slowly changing dimension (SCD) to manage the versioning of data.

The solution must meet the requirements shown in the following table.


Which type of SCD should you use for each table? To answer, drag the appropriate SCD types to the correct tables. Each SCD type 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.

13. You are analyzing the data in a Fabric notebook.

You have a Spark DataFrame assigned to a variable named df.

You need to use the Chart view in the notebook to explore the data manually.

Which function should you run to make the data available in the Chart view?

14. HOTSPOT

You need to resolve the issue with the pricing group classification.

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.

15. DRAG DROP

You are building a solution by using a Fabric notebook.

You have a Spark DataFrame assigned to a variable named df. The DataFrame returns four columns.

You need to change the data type of a string column named Age to integer. The solution must return a DataFrame that includes all the columns.

How should you complete the code? 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.

16. 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.

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

18. DRAG DROP

You are creating a data flow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.

You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.

How should you complete the code? 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.

19. You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2 and a user named User1.

You need to ensure that User1 can perform the following tasks:

- Create a new domain.

- Create two subdomains named subdomain1 and subdomain2.

- Assign Workspace1 to subdomain1.

- Assign Workspace2 to subdomain2.

The solution must follow the principle of least privilege.

Which role should you assign to User1?

20. 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?

21. HOTSPOT

You need to migrate the Research division data for Productline2. 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.

22. HOTSPOT

You have a Fabric tenant that contains a workspace named Workspace1 and a user named DBUser.

Workspace1 contains a lakehouse named Lakehouse1. DBUser does NOT have access to the tenant.

You grant DBUser access to Lakehouse1 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.

23. 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?

24. You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table that has one million Parquet files.

You need to remove files that were NOT referenced by the table during the past 30 days. The solution must ensure that the transaction log remains consistent, and the ACID properties of the table are maintained.

What should you do?

25. DRAG DROP

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

Readings from 100 IoT devices are appended to a Delta table in Lakehouse1. Each set of readings is approximately 25 KB. Approximately 10 GB of data is received daily.

All the table and SparkSession settings are set to the default.

You discover that queries are slow to execute. In addition, the lakehouse storage contains data and log files that are no longer used.

You need to remove the files that are no longer used and combine small files into larger files with a target size of 1 GB per file.

What should you do? To answer, drag the appropriate actions to the correct requirements. Each action 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.

26. You have a Fabric notebook that has the Python code and output shown in the following exhibit.









Which type of analytics are you performing?

27. 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?

28. 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?

29. 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?

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

You plan to use a visual query to merge two tables.

You need to ensure that the query returns all the rows in both tables.

Which type of join should you use?