An analyst writes a nested LOD: { FIXED [Region] : AVG( { FIXED [Region], [Customer ID] : SUM([Sales]) } ) }. The data has many customers per region. What does the outer expression return for each Region?
{ FIXED [Region] :
AVG( { FIXED [Region], [Customer ID] : SUM([Sales]) } ) }- AThe average Sales per customer within the Region, found by averaging each customer's total. Correct
- BThe total Sales for the Region, because the inner SUM and the outer FIXED both aggregate at the Region grain.
- CThe number of distinct customers in the Region, because nesting FIXED expressions counts the inner grain's members.
- DThe average of individual order-line Sales within the Region, identical to a plain AVG([Sales]) by Region.
Why A is correct: The inner FIXED produces one SUM(Sales) per customer per Region, and the outer FIXED then averages those customer totals to give mean sales per customer in each Region.
Why B is wrong: The inner expression is at the finer Region-plus-Customer grain, so the outer AVG averages those per-customer totals rather than summing back to the Region total.
Why C is wrong: Nesting does not convert a SUM into a count, so the result is an average of sales amounts, not a tally of customers.
Why D is wrong: A plain AVG([Sales]) averages order lines, but here the inner SUM first rolls sales up to the customer level, so frequent buyers are weighted differently.