You have a sales table with columns: Region, SalesAmount. You want to calculate total sales per region using an intermediate DAX measure with SUMX and VALUES.
Which DAX measure produces the correct total sales per region?
Think about how VALUES returns unique regions and SUMX iterates over them.
Option A uses VALUES to get unique regions and SUMX to sum sales per region correctly. Option A sums all sales ignoring regions. Option A removes region filter, summing all sales. Option A sums sales row by row without grouping.
You have a sales table with Year and SalesAmount. You want to calculate the year-over-year (YoY) growth percentage using intermediate DAX.
Which DAX measure correctly calculates YoY growth?
Use SAMEPERIODLASTYEAR to get last year's sales for comparison.
Option C correctly calculates the difference between current and last year sales, then divides by last year sales to get growth percentage. Option C subtracts the same value, always zero. Option C divides last year sales by current, reversed. Option C uses NEXTYEAR which is incorrect for YoY.
Consider this DAX measure intended to calculate average sales per customer:
Avg Sales per Customer = AVERAGEX(Customer, SUM(Sales[SalesAmount]))
What is the error in this measure?
Check if the table name used in AVERAGEX exists and is spelled correctly.
Option A is correct because 'Customer' is likely a typo or invalid table name. The table should be 'Customers' or the actual customer table name. Option A is incorrect because SUM(Sales[SalesAmount]) inside AVERAGEX is valid when iterating over customers. Option A is wrong because 'Customer' is used as a table, not a column. Option A is incorrect due to the table name error.
You want to show how sales change over time for different product categories. Which visualization type best communicates this information clearly and accessibly?
Think about how to show trends over time and compare categories.
Option B is best because a stacked area chart shows sales trends over time and how each category contributes. Option B only shows one year, losing trend info. Option B is less visual and harder to interpret trends. Option B is not suitable for time series data.
Which reason best explains why intermediate DAX measures are preferred over calculated columns when solving complex business questions in Power BI?
Think about how measures respond to user interactions and filters.
Option D is correct because measures calculate results on the fly considering filters and slicers, enabling dynamic insights. Option D is wrong; calculated columns increase model size and are less flexible. Option D is incorrect; calculated columns store data physically, measures do not. Option D is false; both can be used in visuals but measures are more flexible.