Microsoft Microsoft Certifications DP-600 Questions & Answers

• Question 11:

  You 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?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  Correct Answer: B

  Explanation: 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.

• Question 12:

  You 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?

  A. Group by

  B. Unpivot columns

  C. Unpivot other columns

  D. Split column

  E. Remove other columns

  Correct Answer: B

  Explanation: 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.

• Question 13:

  You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales. You need to create a diagram of the model. The diagram must contain only the Sales table and related tables. What should you use from Microsoft Power Bl Desktop?

  A. data categories

  B. Data view

  C. Model view

  D. DAX query view

  Correct Answer: C

  Explanation: To create a diagram that contains only the Sales table and related tables, you should use the Model view (C) in Microsoft Power BI Desktop. This view allows you to visualize and manage the relationships between tables within your semantic model. References = Microsoft Power BI Desktop documentation outlines the functionalities available in Model view for managing semantic models.

• Question 14:

  You have a Fabric tenant that uses a Microsoft tower Bl Premium capacity. You need to enable scale-out for a semantic model. What should you do first?

  A. At the semantic model level, set Large dataset storage format to Off.

  B. At the tenant level, set Create and use Metrics to Enabled.

  C. At the semantic model level, set Large dataset storage format to On.

  D. At the tenant level, set Data Activator to Enabled.

  Correct Answer: C

  Explanation: To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. References = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.

• Question 15:

  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.

  A. Microsoft Power BI Desktop

  B. Tabular Editor

  C. Microsoft SQL Server Management Studio (SSMS)

  D. DAX Studio

  Correct Answer: AB

  Explanation: Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.

• Question 16:

  You have a Fabric tenant that contains a warehouse.

  Several times a day. the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse.

  What should you use to identify whether throttling is occurring?

  A. the Capacity settings

  B. the Monitoring hub

  C. dynamic management views (DMVs)

  D. the Microsoft Fabric Capacity Metrics app

  Correct Answer: B

  Explanation: To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. References = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.

• Question 17:

  You have a Fabric tenant that 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:

  DESCRIBE HISTORY customer

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  Explanation: Yes, the DESCRIBE HISTORY statement does meet the goal. It provides information on the history of operations, including maintenance tasks, performed on a Delta table. References = The functionality of the DESCRIBE HISTORY statement can be verified in the Delta Lake documentation.

• Question 18:

  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?

  A. Daily

  B. By the minute

  C. Weekly

  D. Hourly

  Correct Answer: C

  Explanation: You should set Repeat for the schedule to Weekly (C). This allows you to specify the pipeline to run on specific days of the week, in this case, every four hours on Mondays and Fridays. References = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.

• Question 19:

  You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains a table named Table1.

  You are creating a new data pipeline.

  You plan to copy external data to Table1. The schema of the external data changes regularly.

  You need the copy operation to meet the following requirements:

  Replace Table1 with the schema of the external data.

  Replace all the data in Table1 with the rows in the external data.

  You add a Copy data activity to the pipeline. What should you do for the Copy data activity?

  A. From the Source tab, add additional columns.

  B. From the Destination tab, set Table action to Overwrite.

  C. From the Settings tab, select Enable staging

  D. From the Source tab, select Enable partition discovery

  E. From the Source tab, select Recursively

  Correct Answer: B

  Explanation: For the Copy data activity, from the Destination tab, setting Table action to Overwrite (B) will ensure that Table1 is replaced with the schema and rows of the external data, meeting the requirements of replacing both the schema and data of the destination table. References = Information about Copy data activity and table actions in Azure Data Factory, which can be applied to data pipelines in Fabric, is available in the Azure Data Factory documentation.

• Question 20:

  You 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.

  A. T-SQL

  B. DAX EC.

  C. Spark SQL

  D. PySpark

  Correct Answer: CD

  Explanation: 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.