Transpose operations in Power BI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ dax_lod_result

intermediate

Transpose a Table Using DAX

You have a table named SalesData with columns Product, Month, and Sales. You want to create a new table that shows Month as rows and Product as columns with the sum of Sales as values.

Which DAX expression correctly transposes the data?

A TRANSPOSE(VALUES(SalesData[Month]))

B SUMMARIZE(SalesData, SalesData[Month], SalesData[Product], "TotalSales", SUM(SalesData[Sales]))

C SUMMARIZECOLUMNS(SalesData[Month], SalesData[Product], "TotalSales", SUM(SalesData[Sales]))

D UNPIVOT(SalesData, SalesData[Month], SalesData[Product], SalesData[Sales])

💡 Hint

Think about a function that groups by multiple columns and aggregates values.

SUMMARIZECOLUMNS groups by Month and Product and sums Sales. This creates a table suitable for pivoting in visuals.

❓ visualization

intermediate

Best Visualization for Transposed Data

You have transposed sales data where months are rows and products are columns showing sales amounts. Which visualization type best displays this transposed data for easy comparison across products and months?

A Pie Chart

B Matrix Visual

C Line Chart

D Stacked Bar Chart

💡 Hint

Look for a visual that supports rows and columns with values in a grid.

The Matrix Visual in Power BI is designed to display data in rows and columns, perfect for transposed tables.

❓ data_modeling

advanced

Handling Transpose with Many-to-Many Relationships

You have two tables: Products and Sales. The Sales table has multiple entries per product per month. You want to create a transposed summary table showing total sales per product per month. What is the best modeling approach to avoid incorrect aggregation?

A Create a one-to-many relationship from <em>Products</em> to <em>Sales</em> on <em>ProductID</em> and use SUMMARIZECOLUMNS with filters.

B Create a relationship between <em>Products[ProductID]</em> and <em>Sales[ProductID]</em> with many-to-many cardinality and use SUMMARIZECOLUMNS.

C Use CROSSJOIN of <em>Products</em> and <em>Sales</em> and then filter by month.

D Duplicate the <em>Sales</em> table and transpose it manually in Power Query.

💡 Hint

Think about correct relationship types for aggregation.

A one-to-many relationship from Products to Sales ensures correct aggregation of sales per product. Many-to-many can cause double counting.

🔧 Formula Fix

advanced

Debugging a Transpose Measure with Incorrect Results

You wrote this DAX measure to transpose sales data by month and product:

TransposedSales = SUMMARIZE(SalesData, SalesData[Month], SalesData[Product], "TotalSales", SUM(SalesData[Sales]))

But the results show duplicated rows and incorrect totals. What is the likely cause?

A Using SUMMARIZE instead of SUMMARIZECOLUMNS causes incorrect context evaluation.

B The measure is missing a FILTER function to limit months.

C The SalesData table is missing a relationship to the Products table.

D The SUM function is aggregating text values instead of numbers.

💡 Hint

Consider differences between SUMMARIZE and SUMMARIZECOLUMNS in context handling.

SUMMARIZE can cause unexpected row duplication due to context transition issues. SUMMARIZECOLUMNS is preferred for reliable aggregation.

🧠 Conceptual

expert

Understanding Transpose Impact on Performance

When transposing large datasets in Power BI, what is the most important factor affecting report performance?

A The use of slicers on transposed data disables query folding.

B The use of calculated columns instead of measures slows down visuals.

C The presence of relationships between tables causes slow refresh.

D The number of columns after transpose increases cardinality and memory usage.

💡 Hint

Think about how data shape affects storage and processing.

Transposing data often increases columns, which raises cardinality and memory use, impacting performance more than relationships or slicers.