PL-300 Model the data Practice Question
Exhibit
Refer to the exhibit.
```dax
EVALUATE
SUMMARIZECOLUMNS(
'Date'[Year],
'Product'[Category],
"Total Sales", CALCULATE(SUM('Sales'[Amount]), FILTER('Sales', 'Sales'[Amount] > 100))
)
```You are analyzing a DAX query as shown in the exhibit. You need to determine the result set. The model contains tables: Date, Product, and Sales with relationships. Which statement accurately describes the output?
⚠ Common exam trap
The trap here is that candidates often misinterpret the SUMMARIZE function as returning individual rows rather than aggregated groups, or they overlook that the filter condition applies to the underlying Sales rows, not to the aggregated result.
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 query returns total sales per year and category for Amount > 100
The DAX query uses SUMMARIZECOLUMNS to group sales by 'Year' from the Date table and 'Category' from the Product table, then filters the Sales table to include only rows where Amount > 100. The result is a table of total sales (sum of Amount) for each combination of year and category that meets the filter condition.
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 query returns total sales per year and category for Amount > 100
Why this is correct
Correct. SUMMARIZECOLUMNS groups the sales data by the Year and Category columns, and the filter condition Amount > 100 is applied to the base table before aggregation. Therefore, for every distinct Year-Category pair, the query returns the sum of the sales measure computed only from rows that satisfy Amount > 100. This exactly matches the described output of total sales per year and category under that filter.
- ✗
The query returns total sales for each year, ignoring category
Why it's wrong here
This is incorrect because SUMMARIZECOLUMNS explicitly lists both Year and Category as grouping columns, so the query returns one row per unique Year-Category combination, not a yearly total that collapses categories. To ignore category, Category would need to be removed from the GROUP BY list, causing the grouping to be solely by Year. Moreover, the Amount > 100 filter is applied to sales rows, but it does not drop Category from the result; it only limits which rows feed the aggregation.
- ✗
The query returns sales amounts only for products with Amount > 100
Why it's wrong here
This is incorrect because the query does not return individual sales amounts; it returns aggregated totals for each Year and Category combination. The Amount > 100 condition is a filter on the sales rows that contribute to those totals, not a product attribute filter. The output granularity is Year-Category, not per-sale or per-product, so it cannot produce sales amounts for specific products.
- ✗
The query returns total sales for each category, ignoring year
Why it's wrong here
This is incorrect because Year is also a grouping column in the SUMMARIZECOLUMNS call, so the result retains a separate row for each year within every category rather than aggregating across all years. To ignore Year, the query would need to omit Year from the GROUP BY columns and possibly use CALCULATE to override the year filter context. Additionally, the Amount > 100 predicate restricts the underlying data, but it does not change the fact that Year remains part of the grouping granularity.
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