Microsoft Microsoft Certifications DP-600 Questions & Answers

• Question 31:

  You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. 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?

  A. Reference the columns in the Python code by index.

  B. Modify the Sort Column By property for all columns.

  C. Add a unique field to each row.

  D. Modify the Summarize By property for all columns.

  Correct Answer: C

  Explanation: To ensure all rows appear in the Python visual within a Power BI report, option C, adding a unique field to each row, is the correct solution. This will prevent automatic grouping by unique values and allow for all instances of data to be represented in the visual. References = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.

• Question 32:

  You are creating a semantic model in Microsoft Power Bl 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?

  A. PBIP

  B. PBIX

  C. PBIT

  D. PBIDS

  Correct Answer: B

  Explanation: When saving a semantic model to a file that can be edited using the Tabular Model Scripting Language (TMSL) extension for Visual Studio Code, the PBIX (Power BI Desktop) file format is the correct choice. The PBIX format contains the report, data model, and queries, and is the primary file format for editing in Power BI Desktop. References = Microsoft's documentation on Power BI file formats and Visual Studio Code provides further clarification on the usage of PBIX files.

• Question 33:

  You have a Fabric tenant that contains a semantic model named Model1. Model1 uses Import mode. Model1 contains a table named Orders. Orders has 100 million rows and the following fields.

  

  You need to reduce the memory used by Model! and the time it takes to refresh the model. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

  A. Split OrderDateTime into separate date and time columns.

  B. Replace TotalQuantity with a calculated column.

  C. Convert Quantity into the Text data type.

  D. Replace TotalSalesAmount with a measure.

  Correct Answer: AD

  Explanation: To reduce memory usage and refresh time, splitting the OrderDateTime into separate date and time columns (A) can help optimize the model because date/time data types can be more memory-intensive than separate date and time columns. Moreover, replacing TotalSalesAmount with a measure (D) instead of a calculated column ensures that the calculation is performed at query time, which can reduce the size of the model as the value is not stored but calculated on the fly. References = The best practices for optimizing Power BI models are detailed in the Power BI documentation, which recommends using measures for calculations that don't need to be stored and adjusting data types to improve performance.

• Question 34:

  You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a subfolder named Subfolder1 that contains CSV files. You need to convert the CSV files into the delta format that has V-Order optimization enabled. What should you do from Lakehouse explorer?

  A. Use the Load to Tables feature.

  B. Create a new shortcut in the Files section.

  C. Create a new shortcut in the Tables section.

  D. Use the Optimize feature.

  Correct Answer: D

  Explanation: To convert CSV files into the delta format with Z-Order optimization enabled, you should use the Optimize feature (D) from Lakehouse Explorer. This will allow you to optimize the file organization for the most efficient querying. References = The process for converting and optimizing file formats within a lakehouse is discussed in the lakehouse management documentation.

• Question 35:

  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.show()

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Explanation: The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions. References = The usage of the show() function is documented in the PySpark API documentation.

• Question 36:

  You need to provide Power Bl developers with access to the pipeline. The solution must meet the following requirements:

  Ensure that the developers can deploy items to the workspaces for Development and Test.

  Prevent the developers from deploying items to the workspace for Production.

  Follow the principle of least privilege.

  Which three levels of access should you assign to the developers? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

  A. Build permission to the production semantic models

  B. Admin access to the deployment pipeline

  C. Viewer access to the Development and Test workspaces

  D. Viewer access to the Production workspace

  E. Contributor access to the Development and Test workspaces

  F. Contributor access to the Production workspace

  Correct Answer: BDE

  Explanation: To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production. References = The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

• Question 37:

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

  A. a data lake

  B. a warehouse

  C. a lakehouse

  D. an external Hive metaStore

  Correct Answer: C

  Explanation: A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

• Question 38:

  What should you recommend using to ingest the customer data into the data store in the AnatyticsPOC workspace?

  A. a stored procedure

  B. a pipeline that contains a KQL activity

  C. a Spark notebook

  D. a dataflow

  Correct Answer: D

  Explanation: For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. References = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.

• Question 39:

  You have a Microsoft Power B1 report and a semantic model that uses Direct Lake mode. From Power Si 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.

  Hot Area:

  

  Correct Answer:

  

  The Direct Lake fallback behavior is set to: DirectQueryOnly The query for the table visual is executed by using: DirectQuery

  In the context of Microsoft Power BI, when using DirectQuery in Direct Lake mode, there is no caching of data and all queries are sent directly to the underlying data source. The Performance Analyzer tool shows the time taken for different operations, and from the options provided, it indicates that DirectQuery mode is being used for the visuals, which is consistent with the Direct Lake setting. DirectQueryOnly as the fallback behavior ensures that only DirectQuery will be used without reverting to import mode.

• Question 40:

  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 TSQL 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.

  Hot Area:

  

  Correct Answer:

  

  A replica of dbo.Sales is created in the test schema by copying the metadata only. - No Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No Additional data changes to dbo.FactSales will also apply to test.FactSales. - Yes The CREATE TABLE AS CLONE statement creates a copy of an existing table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated. References = CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse