DP-700試験無料問題集「Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric 認定」
HOTSPOT
You have an Azure Event Hubs data source that contains weather data.
You ingest the data from the data source by using an eventstream named Eventstream1. Eventstream1 uses a lakehouse as the destination.
You need to batch ingest only rows from the data source where the City attribute has a value of Kansas. The filter must be added before the destination. The solution must minimize development effort.
What should you use for the data processor and filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You have an Azure Event Hubs data source that contains weather data.
You ingest the data from the data source by using an eventstream named Eventstream1. Eventstream1 uses a lakehouse as the destination.
You need to batch ingest only rows from the data source where the City attribute has a value of Kansas. The filter must be added before the destination. The solution must minimize development effort.
What should you use for the data processor and filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
Explanation:
You have a Fabric workspace named Workspace1 that is connected to a GitHub repository named repo1.
Workspace1 contains the items shown in the following table.
You modify Semantic model 1, Semanticmodel2, and Report2.
You need to commit the changes to repo1.
What is the minimum number of commits you should perform?
Workspace1 contains the items shown in the following table.
You modify Semantic model 1, Semanticmodel2, and Report2.
You need to commit the changes to repo1.
What is the minimum number of commits you should perform?
正解：D
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HOTSPOT
You are processing streaming data from an external data provider.
You have the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
You are processing streaming data from an external data provider.
You have the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
Explanation:
Litware from New York will be displayed at the top of the result set - Yes The data is sorted first by Location in descending order and then by UnitsSold in descending order. Since " New York " is alphabetically the last Location, it will appear first in the result set. Within " New York " , Litware has the highest UnitsSold (1000), so it will be displayed at the top.
Fabrikam in Seattle will have value = 2 in the Rank column - No
The row_rank_dense function assigns dense ranks based on UnitsSold within each location. In " Seattle " :
Contoso has UnitsSold = 300 # Rank 1
Litware has UnitsSold = 100 # Rank 2
Fabrikam also has UnitsSold = 100, so it shares the same rank (2) as Litware.
Litware in San Francisco will have the same value in the Rank column as Litware in New York - No The rank is calculated separately for each location. In " San Francisco " :
Both Relecloud and Litware have UnitsSold = 500, so they share the same rank (1).
In " New York " , Litware has the highest UnitsSold = 1000 # Rank 1.
Since ranks are calculated independently for each location, Litware in San Francisco does not share the same rank as Litware in New York.
You are implementing a medallion architecture in a Fabric lakehouse.
You plan to create a dimension table that will contain the following columns:
* ID
* CustomerCode
* CustomerName
* CustomerAddress
* CustomerLocation
* ValidFrom
* ValidTo
You need to ensure that the table supports the analysis of historical sales data by customer location at the time of each sale Which type of slowly changing dimension (SCD) should you use?
You plan to create a dimension table that will contain the following columns:
* ID
* CustomerCode
* CustomerName
* CustomerAddress
* CustomerLocation
* ValidFrom
* ValidTo
You need to ensure that the table supports the analysis of historical sales data by customer location at the time of each sale Which type of slowly changing dimension (SCD) should you use?
正解：A
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Note: 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 KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.
Reference contains reference data in the following format.
Both tables contain millions of rows.
You have the following KQL queryset.
You need to reduce how long it takes to run the KQL queryset.
Solution: You add the make_list() function to the output columns.
Does this meet the goal?
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 KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.
Reference contains reference data in the following format.
Both tables contain millions of rows.
You have the following KQL queryset.
You need to reduce how long it takes to run the KQL queryset.
Solution: You add the make_list() function to the output columns.
Does this meet the goal?
正解：A
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解説: (GoShiken メンバーにのみ表示されます)
You need to ensure that the authors can see only their respective sales data.
How should you complete the statement? To answer, drag the appropriate values 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.
How should you complete the statement? To answer, drag the appropriate values 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.
正解：
Explanation:
