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Adding an index key and normalizing casing in the data source would help resolve issues with undefined values and errors in a case-sensitive column. The index key can provide a unique identifier, and normalizing casing ensures consistency in the data, which would likely prevent the errors you encountered in the Power BI semantic model. You have a Power BI data model that imports a date table and a sales table from an Azure SQL database data source. The sales table has the following date foreign keys. * Due Date * Order Date * Delivery Date You need to support the analysis of sales over time based on all three dates at the same time. Solution: You create measures that use the USERELATIONSHIP DAX function to filter sales on the inactive relationships between sales table and the date table. Does this meet the goal?
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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 Power BI report that imports a date table and a sales table from an Azure SQL database data source. The sales table has the following date foreign keys: * Due Date * Order Date * Delivery Date You need to support the analysis of sales over time based on all three dates at the same time. Solution; You create measures that use the uSEREIATIOHSHIP DAX function to filter sales on the inactive relationships between the sales table and the date table. Does this meet the goal?

Question 125 - PL-300 discussion

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Note: This question is a part of a series of questions that present the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.

Start of repeated scenario

You have a Microsoft SQL Server database that has the tables shown in the Database Diagram exhibit. (Click the Exhibit.)

You plan to develop a Power BI model as shown in the Power BI Model exhibit. (Click the Exhibit).

You plan to use Power BI to import data from 2013 to 2015.

Product Subcategory [Subcategory] contains NULL values.

End of repeated scenario.

You implement the Power BI model.

You need to add a measure to rank total sales by product. The results must appear as shown in the following table.

Which DAX formula should you use?

A.

Product Ranking= RANKX (Product, [SalesAmount], , DESC, Skip)

Answers
A.

Product Ranking= RANKX (Product, [SalesAmount], , DESC, Skip)

B.

Product Ranking= RANKX (ALL, (‘Product’), [SalesAmount], , DESC, Dense)

Answers
B.

Product Ranking= RANKX (ALL, (‘Product’), [SalesAmount], , DESC, Dense)

C.

Product Ranking= RANKX (ALL, (‘Product’), [SalesAmount], , DESC, Skip)

Answers
C.

Product Ranking= RANKX (ALL, (‘Product’), [SalesAmount], , DESC, Skip)

D.

Product Ranking= RANKX (ALL (‘Product’), [SalesAmount], , Asc, Dense

Answers
D.

Product Ranking= RANKX (ALL (‘Product’), [SalesAmount], , Asc, Dense

Suggested answer: B

Explanation:

Reference: https://msdn.microsoft.com/en-us/library/gg492185.aspx

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asked 05/10/2024
Oleksii Ivanov
45 questions
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