PL-300 Visualize and analyze the data Practice Question
Exhibit
Refer to the exhibit. ```dax Customer Lifetime Value = VAR TotalRevenue = SUM(Sales[Amount]) VAR CustomerCount = DISTINCTCOUNT(Sales[CustomerID]) RETURN DIVIDE(TotalRevenue, CustomerCount, 0) ```
Refer to the exhibit. You have a DAX measure that calculates customer lifetime value (CLV) as total revenue divided by distinct customer count. When you use this measure in a visual with Product category, you notice that the CLV values are higher than expected. What is the most likely reason?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The measure counts customers per category, but customers who buy multiple categories are counted in each category, reducing the denominator
The CLV measure is defined as total revenue divided by distinct customer count. When this measure is used in a visual with Product category, the context filters both the revenue and the customer count to that category. The DISTINCTCOUNT(CustomerID) returns the number of customers who purchased at least one product in that category. If a customer buys multiple categories, they are counted in each category's distinct count. This makes the denominator per category smaller than the total distinct customer base, leading to a higher CLV value than expected. Option A is incorrect because returns would reduce revenue, not cause higher CLV. Option C is incorrect because using COUNTROWS would count transaction rows, not distinct customers, making the denominator larger and CLV smaller. Option D is incorrect because DIVIDE handles division by zero, and the scenario does not involve zero customers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The measure does not filter out returns
Why it's wrong here
Not relevant to the high values.
- ✓
The measure counts customers per category, but customers who buy multiple categories are counted in each category, reducing the denominator
Why this is correct
This inflates CLV per category.
- ✗
The measure should use COUNTROWS instead of DISTINCTCOUNT
Why it's wrong here
COUNTROWS would count each transaction, not distinct customers.
- ✗
The measure is dividing by zero for categories with no customers
Why it's wrong here
DIVIDE returns 0 for division by zero.
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