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질문 # 141
You have a Fabric warehouse named DW1 that contains four staging tables named ProductCategory, ProductSubcategory, Product, and SalesOrder. ProductCategory, ProductSubcategory, and Product are used often in analytical queries.
You need to implement a star schema for DW1. The solution must minimize development effort.
Which design approach should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
질문 # 142
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 change the join type to kind=outer.
Does this meet the goal?
정답：B
설명：
An outer join will include unmatched rows from both tables, increasing the dataset size and processing time. It does not improve query performance.
질문 # 143
You have a Fabric workspace that contains a lakehouse named Lakehousel. Lakehousel contains a table named Status_Target that has the following columns:
* Key
* Status
* LastModified
The data source contains a table named Status.Source that has the same columns as Status_Target. Status.Source is used to populate Status_Target. In a notebook name Notebook!, you load Status_Source to a DataFrame named sourceDF and Status_Target to a DataFrame named targetDF. You need to implement an incremental loading pattern by using Notebook-!. The solution must meet the following requirements:
* For all the matching records that have the same value of key, update the value of LastModified in Status_Target to the value of LastModified in Status_Source.
* Insert all the records that exist in Status_Source that do NOT exist in Status_Target.
* Set the value of Status in Status_Target to inactive for all the records that were last modified more than seven days ago and that do NOT exist in Status.Source.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 144
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.
You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
정답：B,C,E
설명：
In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.
질문 # 145
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.
정답：
설명：
질문 # 146
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