Top Power BI Interview Questions with Answers – Part 4

31. What are tooltips in Power BI?  

Tooltips are small popups that appear when you hover over a data point on a visual. They provide additional context, like values or percentages. You can also create custom tooltip pages to show charts or extra info on hover.

32. How do you use variables in DAX?  

Variables improve readability and performance in DAX. They are declared using VAR and returned using RETURN.  

Example:  

VAR TotalSales = SUM(Sales[Amount])  
RETURN TotalSales * 0.1

33. What are quick measures?  

Quick measures are pre-built DAX calculations that can be created using the UI without writing complex DAX. Example: running totals, percent of grand total, year-to-date, etc.

34. What is the use of FORMAT() in DAX?  

FORMAT() changes the appearance of a value without changing its data type.  

Example:  

FORMAT(TODAY(), "DD-MM-YYYY") // "21-12-2025"

35. What is a calculated table?  

A calculated table is created using a DAX formula instead of being loaded from a data source. It’s useful for intermediate tables, like date tables or filtered subsets.  

Example:  

TopCustomers = FILTER(Sales, Sales[Amount] > 1000)

36. How do you apply conditional formatting in visuals?  

Conditional formatting allows you to change colors, font, or icons based on field values. You can apply it in tables, matrices, or bar charts via the visual formatting pane.

37. What are the different types of filters in Power BI?  

• Visual-level filter

• Page-level filter

• Report-level filter

• Drillthrough filter

Each applies to a different scope in the report.

38. What is a hierarchy in Power BI?  

A hierarchy organizes data in levels (e.g., Year > Quarter > Month). It enables drilldown functionality in visuals.

39. How does Power BI handle large datasets?  

Power BI uses compression, aggregations, and in-memory storage. You can improve performance with DirectQuery, incremental refresh, and data modeling techniques.

40. What is Incremental Refresh?  

It updates only new or changed data instead of reloading the entire dataset. This is crucial for large datasets and improves refresh performance.