1. How would you identify customers who purchased in every year available in the dataset?
Count distinct years for each customer and compare with total years in the dataset.
Example:
{ FIXED [Customer ID] : COUNTD(YEAR([Order Date])) }
=
{ FIXED : COUNTD(YEAR([Order Date])) }
2. How would you calculate the percentage of profit contributed by each category?
Divide category profit by total profit.
Example:
SUM([Profit])
/
TOTAL(SUM([Profit]))
Format the result as a percentage.
3. How would you identify customers whose latest order was a loss-making order?
Find the latest order and check if profit is negative.
Example:
[Order Date] =
{ FIXED [Customer ID] : MAX([Order Date]) }
Then:
SUM([Profit]) < 0
4. How would you calculate the rolling 3-month average profit?
Use a moving window average over the current and previous two months.
Example:
WINDOW_AVG(SUM([Profit]),-2,0)
5. How would you identify products sold in more than 50% of all regions?
Compare product region count against total regions.
Example:
COUNTD([Region])
/
{ FIXED : COUNTD([Region]) }
> 0.5
6. How would you calculate the average sales per customer within each region?
Divide regional sales by distinct customers in that region.
Example:
SUM([Sales])
/
COUNTD([Customer ID])
Place Region in the view to calculate it region-wise.
