Quiz / Skill Check
Q1. Which DAX pattern is typically used to build a running total measure?
Type: Multiple Choice
- SUM by itself, with no filter modification
- CALCULATE combined with a FILTER over dates less than or equal to the current date in context
- COUNTROWS with no arguments
- AVERAGE wrapped in RANKX
Correct Answer: CALCULATE combined with a FILTER over dates less than or equal to the current date in context
Why: A running total re-evaluates an aggregation (like SUM) across all dates up to and including the current one in context, which requires CALCULATE to modify the filter context with a condition such as ‘Date'[Date] <= MAX(‘Date'[Date]).
Q2. Using VAR and RETURN in a DAX measure changes the calculation result compared to writing the same logic without variables.
Type: True/False
Correct Answer: False
Why: VAR/RETURN doesn’t change what the measure calculates — it only makes the formula easier to read, debug, and reuse a value that would otherwise need to be repeated. The underlying logic and result are identical; variables are a readability and performance convenience, not a different calculation.
Q3. What does RANKX need, at minimum, to rank doctors by revenue?
Type: Short Answer
Correct Answer: A table (or table expression) to rank over and an expression to rank by, e.g. RANKX(<table>, <expression>) — often with an order (ASC/DESC) added.
Why: RANKX(table, expression, [value], [order], [ties]) evaluates the expression for every row of the given table, then returns where the current context’s value ranks among them. Without a table to compare against, there’s nothing to rank relative to.
Q4. What is one practical use of a time intelligence function like SAMEPERIODLASTYEAR in this hospital dataset?
Type: Multiple Choice
- Counting the total number of doctors
- Comparing this year’s revenue or appointment volume to the same period last year to spot trends
- Renaming a column
- Building a relationship between two tables
Correct Answer: Comparing this year’s revenue or appointment volume to the same period last year to spot trends
Why: Time intelligence functions shift the filter context to a different, comparable period (like the same months a year earlier), making it possible to build year-over-year or period-over-period comparisons — useful for spotting whether no-shows or revenue are trending up or down.