Calculated columns vs measures in Power BI - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Difference in Calculation Timing

When using Power BI, what is the main difference in when calculated columns and measures perform their calculations?

A Calculated columns compute values dynamically during report interaction; measures compute values during data refresh.

B Both calculated columns and measures compute values only when the report is published.

C Both calculated columns and measures compute values only during data refresh.

D Calculated columns compute values during data refresh; measures compute values dynamically during report interaction.

💡 Hint

Think about when the data is stored versus when it is calculated on the fly.

Calculated columns are computed once when the data is loaded or refreshed and stored in the model. Measures calculate results dynamically based on user interactions like filters or slicers.

❓ dax_lod_result

intermediate

Measure Calculation with Filter Context

Given a Sales table with columns: Product, Quantity, and Price, and a measure defined as Total Sales = SUMX(Sales, Sales[Quantity] * Sales[Price]), what will be the result of Total Sales when a filter is applied to show only Product = 'A'?

A Sum of Quantity * Price for all products, ignoring the filter.

B Sum of Quantity * Price only for Product 'A'.

C Sum of Quantity only for Product 'A', ignoring Price.

D Sum of Price only for Product 'A', ignoring Quantity.

💡 Hint

Measures respect filters applied in the report visuals.

Measures calculate results based on the current filter context, so Total Sales will sum Quantity * Price only for Product 'A'.

❓ data_modeling

advanced

Choosing Between Calculated Column and Measure

You want to create a new column that categorizes sales as 'High' if the total sales amount exceeds 1000, otherwise 'Low'. The categorization should be available for each row in the Sales table. Which option is best?

A Create a calculated column with an IF statement checking if Quantity * Price > 1000.

B Create a measure with an IF statement checking if SUM(Quantity * Price) > 1000.

C Create a calculated column with an IF statement checking if SUM(Quantity * Price) > 1000.

D Create a measure with an IF statement checking if Quantity * Price > 1000.

💡 Hint

Think about whether the categorization needs to be stored per row or calculated dynamically.

Since the categorization is per row, a calculated column using row-level values (Quantity * Price) is appropriate. Measures aggregate data and cannot create row-level categories.

🔧 Formula Fix

advanced

Why Does This Measure Return Unexpected Results?

Consider this measure:
Average Price = AVERAGE(Sales[Price])
When used in a report with Product filter, it returns the average price for all products, ignoring the filter. What is the likely cause?

A The measure uses ALL() function inside, removing filters.

B The measure is defined as a calculated column, not a measure.

C The Price column has blank values causing errors.

D The report visual does not support filtering.

💡 Hint

Check if the measure code includes functions that override filters.

If the measure uses ALL(Sales) or similar, it removes filters, causing the average to ignore the product filter.

❓ visualization

expert

Best Visualization for Comparing Calculated Column and Measure Results

You have a calculated column 'Profit Category' with values 'High', 'Medium', 'Low' per row, and a measure 'Total Profit' that sums profit dynamically. You want to show how total profit varies by profit category in a report. Which visualization is best?

A Pie chart showing count of rows per 'Profit Category'.

B Table visual showing 'Profit Category' and 'Total Profit' side by side.

C Stacked bar chart with 'Profit Category' on axis and 'Total Profit' as values.

D Line chart showing 'Total Profit' over time.

💡 Hint

Think about how to compare categories with aggregated values visually.

A stacked bar chart effectively shows total profit by profit category, combining the categorical column and measure aggregation.