15 Power BI DAX Interview Questions and Answers

Power BI DAX (Data Analysis Expressions) is a powerful formula language used in Microsoft Power BI for data modeling and analysis. It enables users to create custom calculations, aggregations, and data transformations, making it an essential skill for anyone working with data visualization and business intelligence. DAX’s versatility and efficiency allow for the creation of complex data models and insightful reports, which are crucial for data-driven decision-making.

This guide offers a curated selection of DAX-related interview questions designed to test and enhance your understanding of this critical tool. By familiarizing yourself with these questions, you will be better prepared to demonstrate your proficiency in DAX and showcase your ability to leverage Power BI for impactful data analysis.

Power BI DAX Interview Questions and Answers

1. Explain how you would use DAX to create a calculated column that categorizes sales into “High,” “Medium,” and “Low” based on sales amount.

DAX (Data Analysis Expressions) is a formula language used in Power BI, Excel, and other Microsoft data analysis tools to create custom calculations and aggregations. To categorize sales into “High,” “Medium,” and “Low” based on sales amount, you can use the SWITCH or IF function.

Example using SWITCH:

SalesCategory = 
SWITCH(
    TRUE(),
    Sales[SalesAmount] > 1000, "High",
    Sales[SalesAmount] > 500, "Medium",
    "Low"
)

Alternatively, using nested IF statements:

SalesCategory = 
IF(
    Sales[SalesAmount] > 1000, "High",
    IF(
        Sales[SalesAmount] > 500, "Medium",
        "Low"
    )
)

2. Describe how you would use DAX to calculate the running total of sales over time.

To calculate the running total of sales over time, use DAX functions to create a measure that accumulates sales values as you move through dates. The key functions are CALCULATE, SUM, and FILTER.

Example:

RunningTotalSales = 
CALCULATE(
    SUM(Sales[SalesAmount]),
    FILTER(
        ALLSELECTED(Sales[Date]),
        Sales[Date] <= MAX(Sales[Date])
    )
)

3. Write a DAX formula to calculate the average sales per customer.

To calculate the average sales per customer, use the following formula:

AverageSalesPerCustomer = 
    AVERAGEX(
        VALUES(Sales[CustomerID]),
        CALCULATE(SUM(Sales[SalesAmount]))
    )

This formula creates a table of unique customer IDs and calculates the total sales amount for each, then computes the average.

4. How would you use DAX to calculate the year-to-date sales?

To calculate Year-to-Date (YTD) sales, use the TOTALYTD function:

YTD Sales = 
TOTALYTD(
    SUM(Sales[SalesAmount]),
    Sales[Date]
)

TOTALYTD accumulates the total from the beginning of the year to the specified date.

5. How would you use DAX to concatenate two text columns with a space in between?

To concatenate two text columns with a space in between, use the & operator:

NewColumn = Table[Column1] & " " & Table[Column2]

6. Write a DAX formula to create a table that contains only the top 10 products by sales.

To create a table containing only the top 10 products by sales, use the TOPN function:

Top10Products = 
TOPN(
    10,
    SUMMARIZE(
        Sales,
        Products[ProductName],
        "TotalSales", SUM(Sales[SalesAmount])
    ),
    [TotalSales],
    DESC
)

SUMMARIZE groups sales data by product name and calculates total sales, while TOPN returns the top 10 products.

7. How would you use DAX to calculate the sum of sales for each product category?

To calculate the sum of sales for each product category, use SUMMARIZE and SUM:

TotalSalesByCategory = 
SUMMARIZE(
    Sales,
    Sales[ProductCategory],
    "Total Sales", SUM(Sales[SalesAmount])
)

SUMMARIZE groups data by product category and calculates total sales.

8. How would you use DAX to filter a table to show only the rows where sales are greater than the average sales?

To filter a table to show only rows where sales are greater than the average, use FILTER and CALCULATE:

FilteredSalesTable = 
FILTER(
    SalesTable,
    SalesTable[Sales] > CALCULATE(AVERAGE(SalesTable[Sales]))
)

FILTER iterates over the table, including only rows where sales exceed the average.

9. Write a DAX formula that uses variables to simplify a complex calculation.

Use variables in DAX to simplify complex calculations by storing intermediate results:

Total Sales After Discount and Tax = 
VAR DiscountRate = 0.1
VAR TaxRate = 0.08
VAR SalesAmount = SUM(Sales[Amount])
VAR DiscountedAmount = SalesAmount * (1 - DiscountRate)
VAR TaxAmount = DiscountedAmount * TaxRate
RETURN
DiscountedAmount + TaxAmount

Variables break down calculations into smaller parts, improving readability and performance.

10. Write a DAX formula to calculate the moving average of sales over the last 6 months.

To calculate the moving average of sales over the last 6 months, use:

Moving Average Sales = 
CALCULATE(
    AVERAGEX(
        DATESINPERIOD(
            'Date'[Date], 
            LASTDATE('Date'[Date]), 
            -6, 
            MONTH
        ), 
        [Total Sales]
    )
)

DATESINPERIOD creates a date range for the last 6 months, and AVERAGEX calculates the average sales.

11. How would you use DAX to create a dynamic title that changes based on user selections?

Create dynamic titles that change based on user selections using measures and conditional logic:

DynamicTitle = 
VAR SelectedCategory = SELECTEDVALUE('Category'[CategoryName], "All Categories")
RETURN "Sales Report for " & SelectedCategory

SELECTEDVALUE captures user selections, and the RETURN statement forms the dynamic title.

12. What are some best practices for writing DAX formulas when working with large datasets?

When working with large datasets, ensure optimal performance by following best practices:

– Use variables to improve readability and performance.
– Apply filters early to reduce data processing.
– Avoid iterative functions; use direct aggregations.
– Leverage built-in functions for context modification.
– Minimize calculated columns; use measures instead.
– Optimize the data model by removing unnecessary elements.
– Pre-aggregate data to reduce processing load.

13. How do you manage many-to-many relationships in Power BI using DAX?

Manage many-to-many relationships using functions like TREATAS:

Sales = SUMX(
    TREATAS(
        VALUES(Orders[ProductID]),
        Products[ProductID]
    ),
    Orders[Quantity] * Products[Price]
)

TREATAS creates a virtual relationship between tables for accurate aggregation.

14. How would you use DAX to create a measure that only includes sales from the top 5 customers by revenue?

To create a measure that includes sales from the top 5 customers by revenue, use RANKX, CALCULATE, and FILTER:

Top5CustomerSales = 
CALCULATE(
    SUM(Sales[Revenue]),
    TOPN(
        5,
        SUMMARIZE(
            Sales,
            Sales[CustomerID],
            "TotalRevenue", SUM(Sales[Revenue])
        ),
        [TotalRevenue],
        DESC
    )
)

SUMMARIZE creates a table summarizing total revenue for each customer, and TOPN selects the top 5.

15. How do you handle errors in DAX calculations?

Handle errors in DAX calculations using functions like IFERROR and ISERROR:

SalesPerUnit = IFERROR([Total Sales] / [Total Units], 0)

IFERROR returns a specified value if an error is detected, ensuring calculations do not fail. Use ISERROR with IF for more complex error-handling logic:

SalesPerUnit = IF(ISERROR([Total Sales] / [Total Units]), "Error", [Total Sales] / [Total Units])

This approach provides meaningful fallback values or messages.