Microsoft Microsoft Certifications DP-600 Questions & Answers

• Question 21:

  You have a Fabric tenant named Tenant1 that contains a workspace named WS1. WS1 uses a capacity named C1 and contains a dawset named DS1. You need to ensure read- write access to DS1 is available by using the XMLA endpoint. What should be modified first?

  A. the DS1 settings

  B. the WS1 settings

  C. the C1 settings

  D. the Tenant1 settings

  Correct Answer: C

  Explanation: To ensure read-write access to DS1 is available by using the XMLA endpoint, the C1 settings (which refer to the capacity settings) should be modified first. XMLA endpoint configuration is a capacity feature, not specific to individual datasets or workspaces. References = The configuration of XMLA endpoints in Power BI capacities is detailed in the Power BI documentation on dataset management.

• Question 22:

  You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

  

  You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

  

  Which code should you run to populate the results DataFrame? A)

  

  B)

  

  C)

  

  D)

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  Correct Answer: A

  Explanation: The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.

• Question 23:

  You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.

  You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.

  When you initiate a refresh of Model1, the refresh fails after running out of resources.

  What is a possible cause of the failure?

  A. Query folding is occurring.

  B. Only refresh complete days is selected.

  C. XMLA Endpoint is set to Read Only.

  D. Query folding is NOT occurring.

  E. The data type of the column used to partition the data has changed.

  Correct Answer: D

  Explanation: A possible cause for the failure is that query folding is NOT occurring (D). Query folding helps optimize refresh by pushing down the query logic to the source system, reducing the amount of data processed and transferred, hence conserving resources. References = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.

• Question 24:

  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?

  A. ReadTable3.

  B. Update the data Table3.

  C. ReadTable2.

  D. Update the data in Table1.

  Correct Answer: D

• Question 25:

  You have a Fabric tenant that contains a warehouse.

  A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.

  You need to identify what is preventing the report query from completing.

  Which dynamic management view (DMV) should you use?

  A. sys.dm-exec_requests

  B. sys.dn_.exec._sessions

  C. sys.dm._exec._connections

  D. sys.dm_pdw_exec_requests

  Correct Answer: D

  Explanation: The correct DMV to identify what is preventing the report query from completing is sys.dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. References = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.

• Question 26:

  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?

  A. displayMTML

  B. show

  C. write

  D. display

  Correct Answer: D

  Explanation: The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. References = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.

• Question 27:

  You have source data in a folder on a local computer.

  You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:

  Support the use of dataflows to load and append data to the data store.

  Ensure that Delta tables are V-Order optimized and compacted automatically.

  Which type of data store should you use?

  A. a lakehouse

  B. an Azure SQL database

  C. a warehouse

  D. a KQL database

  Correct Answer: A

  Explanation: A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.

• Question 28:

  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.

  A. Create a shortcut in the Files section.

  B. Use the Parquet format

  C. Use the CSV format.

  D. Create a shortcut in the Tables section.

  E. Use the delta format.

  Correct Answer: BD

  Explanation: You should use the Parquet format (B) for the sales data files because it is optimized for performance with large datasets in analytical processing and create a shortcut in the Tables section (D) to facilitate SQL queries through the lakehouse's SQL endpoint. References = The best practices for working with file formats and shortcuts in a lakehouse environment are covered in the lakehouse and SQL endpoint documentation provided by the cloud data platform services.

• Question 29:

  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?

  A. Yes

  B. No

  Correct Answer: B

  Explanation: The df.explain() method does not meet the goal of evaluating data to calculate statistical functions. It is used to display the physical plan that Spark will execute. References = The correct usage of the explain() function can be found in the PySpark documentation.

• Question 30:

  You have a Fabric tenant tha1 contains a takehouse 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?

  A. Yes

  B. No

  Correct Answer: B

  Explanation: No, the REFRESH TABLE statement does not provide information on whether maintenance tasks were performed. It only updates the metadata of a table to reflect any changes on the data files. References = The use and effects of the REFRESH TABLE command are explained in the Spark SQL documentation.