FILTER versus a column filter
A column filter such as Product[Category] = "Bikes" replaces the filter on that column. In the Clothing row of a matrix it throws Clothing away and shows bike sales, which is exactly what you want for a reference column next to each category.
FILTER(Product, Product[Category] = "Bikes") is different: it iterates the products visible in the cell. In the Clothing row there are no bikes to iterate, so the result is empty. Table filters keep the context they start from; column filters replace it.
Your task
Write Bike Sales that shows the sales of bikes in every category row, so each row can be compared with bikes.
About this exercise
You practise table filters and what they keep with CALCULATE and FILTER. The exercise runs on a small bike-shop model with a Sales fact table and Product, Customer and Date dimensions, the same star schema in every lesson.
Your measure, Bike Sales, fills a Power BI-style matrix grouped by Product[Category] and Date[Year]. As in a real report, each cell evaluates the measure in its own filter context, and the subtotals and the grand total are evaluated again in theirs rather than added up. When you check your answer, every cell is compared with the expected value, wrong cells are marked, and the hint is about the mistake you made.
Measures that already exist in the model: Sales Amount. Reference them in square brackets, for example [Sales Amount].
Where you start
Bike Sales =The result to build
| Year | |||
|---|---|---|---|
| Category | 2024 | 2025 | Total |
| Accessories | 102,364.50 | 107,241.50 | 209,606.00 |
| Bikes | 102,364.50 | 107,241.50 | 209,606.00 |
| Clothing | 102,364.50 | 107,241.50 | 209,606.00 |
| Total | 102,364.50 | 107,241.50 | 209,606.00 |
Last updated 10 November 2026
Opening the exercise…