This page was exported from Free Exam Dumps Collection [ http://free.examcollectionpass.com ] Export date:Thu Mar 13 17:17:08 2025 / +0000 GMT ___________________________________________________ Title: [Q21-Q45] Microsoft DP-600 Dumps Updated [Dec-2024] Get 100% Real Exam Questions! --------------------------------------------------- [Dec-2024] Pass Microsoft DP-600 Exam in First Attempt Guaranteed! Full DP-600 Practice Test and 87 unique questions with explanations waiting just for you, get it now! NEW QUESTION 21You have a data warehouse that contains a table named Stage. Customers. Stage-Customers contains all the customer record updates from a customer relationship management (CRM) system. There can be multiple updates per customer You need to write a T-SQL query that will return the customer ID, name, postal code, and the last updated time of the most recent row for each customer ID.How should you complete the code? To answer, select the appropriate options in the answer area, NOTE Each correct selection is worth one point. Explanation:* In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).* In the WHERE clause, choose WHERE X = 1.To select the most recent row for each customer ID, you use the ROW_NUMBER() window function partitioned by CustomerID and ordered by LastUpdated in descending order. This will assign a row number of 1 to the most recent update for each customer. By selecting rows where the row number (X) is 1, you get the latest update per customer.References =* Use the OVER clause to aggregate data per partition* Use window functionsNEW QUESTION 22You have a data warehouse that contains a table named Stage. Customers. Stage-Customers contains all the customer record updates from a customer relationship management (CRM) system. There can be multiple updates per customer You need to write a T-SQL query that will return the customer ID, name, postal code, and the last updated time of the most recent row for each customer ID.How should you complete the code? To answer, select the appropriate options in the answer area, NOTE Each correct selection is worth one point. Explanation:* In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).* In the WHERE clause, choose WHERE X = 1.To select the most recent row for each customer ID, you use the ROW_NUMBER() window function partitioned by CustomerID and ordered by LastUpdated in descending order. This will assign a row number of 1 to the most recent update for each customer. By selecting rows where the row number (X) is 1, you get the latest update per customer.References =* Use the OVER clause to aggregate data per partition* Use window functionsNEW QUESTION 23You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.You have a Microsoft Power Bl report named Report1 that uses the model. Report! contains visuals on multiple pages.You need to reduce the query execution time for the visuals on all the pages.What are two features that you can use? Each correct answer presents a complete solution.NOTE: Each correct answer is worth one point.  user-defined aggregations  automatic aggregation  query caching  OneLake integration User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.NEW QUESTION 24You 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?  Yes  No 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.NEW QUESTION 25You need to design a semantic model for the customer satisfaction report.Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point. Explanation:For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS.Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user’s credentials.Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.Based on these considerations, the selections should be:* Authentication method: Service principal authentication* Mode: DirectQueryNEW QUESTION 26You have a Microsoft Power Bl report named Report1 that uses a Fabric semantic model.Users discover that Report1 renders slowly.You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.What will provide the greatest reduction in the rendering duration of Report1?  Change the visual type of Orders By Dale.  Enable automatic page refresh.  Optimize the DAX query of Orders By Date by using DAX Studio.  Reduce the number of visuals in Report1. Based on the duration breakdown provided, the major contributor to the rendering duration is categorized as“Other,” which is significantly higher than DAX Query and Visual display times. This suggests that the issue is less likely with the DAX calculation or visual rendering times and more likely related to model performance or the complexity of the visual. However, of the options provided, optimizing the DAX query can be a crucial step, even if “Other” factors are dominant. Using DAX Studio, you can analyze and optimize the DAX queries that power your visuals for performance improvements. Here’s how you might proceed:* Open DAX Studio and connect it to your Power BI report.* Capture the DAX query generated by the Orders By Date visual.* Use the Performance Analyzer feature within DAX Studio to analyze the query.* Look for inefficiencies or long-running operations.* Optimize the DAX query by simplifying measures, removing unnecessary calculations, or improving iterator functions.* Test the optimized query to ensure it reduces the overall duration.References: The use of DAX Studio for query optimization is a common best practice for improving Power BI report performance as outlined in the Power BI documentation.NEW QUESTION 27You 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. Explanation:The correct order for the DAX expression would be:* DEFINE VAR _SalesSince = DATE ( 2023, 12, 01 )* EVALUATE* FILTER (* SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),* Store[OpenDate] >= _SalesSince )In this DAX query, you’re defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1, 2023, are included in the results.References =* DAX FILTER Function* DAX SUMMARIZE FunctionNEW QUESTION 28You 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. NEW QUESTION 29You 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. NEW QUESTION 30You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.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. Explanation:* Add the pickupDate column: .withColumn(“pickupDate”, df[“pickupDateTime”].cast(“date”))* Filter the DataFrame: .filter(“fareAmount > 0 AND fareAmount < 100”)In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use the .cast(“date”) function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than 0 and less than 100, thus ensuring only positive values less than 100 are included.NEW QUESTION 31You 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?  Reference the columns in the Python code by index.  Modify the Sort Column By property for all columns.  Add a unique field to each row.  Modify the Summarize By property for all columns. 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.NEW QUESTION 32You 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 lakehouse  an Azure SQL database  a warehouse  a KQL database NEW QUESTION 33You 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. Explanation:The correct order for the DAX expression would be:* DEFINE VAR _SalesSince = DATE ( 2023, 12, 01 )* EVALUATE* FILTER (* SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),* Store[OpenDate] >= _SalesSince )In this DAX query, you’re defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1, 2023, are included in the results.References =* DAX FILTER Function* DAX SUMMARIZE FunctionNEW QUESTION 34You 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.  Create a shortcut in the Files section.  Use the Parquet format  Use the CSV format.  Create a shortcut in the Tables section.  Use the delta format. 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.NEW QUESTION 35You have a Microsoft Power Bl semantic model.You plan to implement calculation groups.You need to create a calculation item that will change the context from the selected date to month-to-date (MTD).How should you complete the DAX expression? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point. Explanation:To create a calculation item that changes the context from the selected date to month-to-date (MTD), the appropriate DAX expression involves using the CALCULATE function to alter the filter context and the DATESMTD function to specify the month-to-date context.The correct completion for the DAX expression would be:* In the first dropdown, select CALCULATE.* In the second dropdown, select SELECTEDMEASURE.This would create a DAX expression in the form:CALCULATE(SELECTEDMEASURE(),DATESMTD(‘Date'[DateColumn]))NEW QUESTION 36You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.Report1 is slow to render. You suspect that an inefficient DAX query is being executed.You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. Explanation:To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:* From Performance analyzer, capture a recording.* View the Server Timings tab.* Enable Query Timings and Server Timings. Run the query.* View the Query Timings tab.* Sort the Duration (ms) column in descending order by DAX query time.NEW QUESTION 37You 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?         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.NEW QUESTION 38You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.You have 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. Explanation:* The Spark engine will read only the ‘SalesOrderNumber’, ‘OrderDate’, ‘CustomerName’, ‘UnitPrice’ columns from Sales_raw.csv. – Yes* Removing the partition will reduce the execution time of the query. – No* Adding inferSchema=’true’ to the options will increase the execution time of the query. – Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.NEW QUESTION 39You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.You need to transform the date columns into attribute-value pairs, where columns become rows.You select the VendorlD column.Which transformation should you select from the context menu of the VendorlD column?  Group by  Unpivot columns  Unpivot other columns  Split column  Remove other columns The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.NEW QUESTION 40You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace. You need to use the model to generate predictions by using the predict function in a fabric notebook. Which two languages can you use to perform model scoring? Each correct answer presents a complete solution. NOTE:Each correct answer is worth one point.  T-SQL  DAX EC.  Spark SQL  PySpark The two languages you can use to perform model scoring in a Fabric notebook using the predict function are Spark SQL (option C) and PySpark (option D). These are both part of the Apache Spark ecosystem and are supported for machine learning tasks in a Fabric environment. References = You can find more information about model scoring and supported languages in the context of Fabric notebooks in the official documentation on Azure Synapse Analytics.NEW QUESTION 41You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.You need to transform the date columns into attribute-value pairs, where columns become rows.You select the VendorlD column.Which transformation should you select from the context menu of the VendorlD column?  Group by  Unpivot columns  Unpivot other columns  Split column  Remove other columns NEW QUESTION 42You to need 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. Explanation:* Data Engineers: Read All SQL analytics endpoint data* Data Analysts: Read All Apache Spark* Data Scientists: Read All SQL analytics endpoint dataThe permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:* Data Engineers need read and write access but not to datasets or reports.* Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.* Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.NEW QUESTION 43You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?         The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = ‘2023’ part of the HAVING clause ensures that only records from the year 2023 are included.References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.NEW QUESTION 44Note: 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 semantic model named Model1.You discover that the following query performs slowly against Model1.You need to reduce the execution time of the query.Solution: You replace line 4 by using the following code:Does this meet the goal?  Yes  No NEW QUESTION 45You have a Fabric workspace named Workspace1 and an Azure Data Lake Storage Gen2 account named storage”!. Workspace1 contains a lakehouse named Lakehouse1.You need to create a shortcut to storage! in Lakehouse1.Which connection and endpoint should you specify? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point. Explanation:When creating a shortcut to an Azure Data Lake Storage Gen2 account in a lakehouse, you should use the abfss (Azure Blob File System Secure) connection string and the dfs (Data Lake File System) endpoint. The abfss is used for secure access to Azure Data Lake Storage, and the dfs endpoint indicates that the Data Lake Storage Gen2 capabilities are to be used. Loading … Get Latest DP-600 Dumps Exam Questions in here: https://www.examcollectionpass.com/Microsoft/DP-600-practice-exam-dumps.html --------------------------------------------------- Images: https://free.examcollectionpass.com/wp-content/plugins/watu/loading.gif https://free.examcollectionpass.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-12-03 13:29:22 Post date GMT: 2024-12-03 13:29:22 Post modified date: 2024-12-03 13:29:22 Post modified date GMT: 2024-12-03 13:29:22