Which of the following changes will most effectively reduce the size of a Power BI data model?
- A. Change columns storing whole numbers from Decimal Number to Whole Number data type.
- B. Convert text columns to Date/Time data type.
- C. Replace numeric columns with text columns.
- D. Use Fixed Decimal Number data type for all numeric columns.
Think about how Power BI stores numbers internally and which data types use less space.
Whole Number data type uses less memory than Decimal Number because it stores integers more efficiently. Changing decimal columns that only contain whole numbers to Whole Number reduces model size.
Given a large sales table, you want to create a measure that counts distinct customers only for sales in the current year to reduce model size impact. Which DAX measure will produce the correct result?
Measure = CALCULATE(DISTINCTCOUNT(Sales[CustomerID]), YEAR(Sales[Date]) = YEAR(TODAY()))
Remember that CALCULATE with FILTER applies row context correctly for filtering.
Option A uses CALCULATE with FILTER to apply a row context filter for the current year, correctly counting distinct customers only for that year. Option A is invalid because DISTINCTCOUNT cannot take a table as argument. Option A filters only today's date, not the whole year. Option A counts all customers without filtering.
You want to create a Power BI dashboard that clearly shows the impact of reducing model size by removing unused columns and optimizing data types. Which visualization approach best communicates this?
Think about how to compare sizes visually and by category.
A stacked bar chart comparing model size before and after optimization by category clearly shows where size reductions happened. Pie charts and line charts do not effectively communicate size changes. A table listing columns is informative but less visual and harder to interpret quickly.
A DAX measure uses the following code to calculate total sales but causes the model size to grow unexpectedly. What is the main issue?
Total Sales = SUMX(VALUES(Sales[OrderID]), Sales[Quantity] * Sales[Price])
Consider how VALUES works and what it returns.
VALUES(Sales[OrderID]) returns a distinct list of OrderIDs, which can be very large. SUMX iterates over this table, creating a large intermediate table in memory, increasing model size. Using SUM(Sales[Quantity] * Sales[Price]) or a simpler measure reduces memory usage.
Your Power BI model has multiple large tables with millions of rows. You want to reduce model size without losing critical analysis capabilities. Which combined approach is best?
Think about practical ways to reduce size while keeping analysis possible.
Removing unused columns and converting numeric columns to Whole Number reduces size. Aggregating data before loading reduces row count. This combined approach balances size reduction and analysis needs. Other options either lose important data, increase size, or reduce performance.