Bar and column charts in Power BI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Choosing the Right Chart Type for Comparison

You have sales data for different product categories over several years. You want to compare the total sales of each category side by side for a single year. Which chart type is best suited for this?

A Stacked column chart showing sales per category stacked by year

B Clustered column chart showing sales per category for the selected year

C 100% stacked bar chart showing sales percentage per category across years

D Line chart showing sales trend per category over years

💡 Hint

Think about showing categories side by side for one year, not trends or percentages.

A clustered column chart places categories side by side for easy comparison in a single year. Stacked charts combine values, making side-by-side comparison harder. Line charts show trends over time, not category comparison for one year.

❓ dax_lod_result

intermediate

Calculate Total Sales for Selected Year

Given a sales table with columns SalesAmount and Year, which DAX measure correctly calculates total sales for the year 2023?

A Total Sales 2023 = SUM(Sales[SalesAmount]) WHERE Sales[Year] = 2023

B Total Sales 2023 = SUMX(FILTER(Sales, Sales[Year] = 2023), Sales[SalesAmount])

C Total Sales 2023 = CALCULATE(SUM(Sales[SalesAmount]), FILTER(Sales, Sales[Year] = 2023))

D Total Sales 2023 = CALCULATE(SUM(Sales[SalesAmount]), Sales[Year] = 2023)

💡 Hint

Remember that CALCULATE needs a filter expression wrapped properly.

Option C uses CALCULATE with a proper filter function to sum sales only for 2023. Option C tries to filter by column equality directly, which is invalid syntax. Option C uses SUMX with FILTER which works but is less efficient. Option C uses invalid syntax with WHERE.

❓ data_modeling

advanced

Optimizing Data Model for Bar Chart Performance

You have a large sales dataset with millions of rows. You want to create a bar chart showing sales by product category. Which data modeling approach improves performance the most?

A Create a separate dimension table for product categories and relate it to the sales fact table

B Duplicate product category data inside the sales table to avoid joins

C Keep all sales data in one large flat table and create the bar chart directly

D Use calculated columns in the sales table to categorize products on the fly

💡 Hint

Think about star schema and reducing data duplication.

Creating a separate dimension table for product categories and relating it to the sales fact table reduces data duplication and improves query performance. Keeping all data in one large table or duplicating category data increases size and slows queries. Calculated columns add processing overhead.

🔧 Formula Fix

advanced

Fixing Incorrect Bar Chart Axis Labels

You created a bar chart showing sales by region, but the axis labels show numbers instead of region names. What is the most likely cause?

A The region field used is a numeric ID instead of the region name

B The sales measure is not aggregated properly

C The chart type does not support text labels on the axis

D The data source is missing region data

💡 Hint

Check what field is used on the axis.

If the axis field is a numeric ID instead of the descriptive region name, the chart will show numbers. Aggregation or chart type does not affect axis labels. Missing data would cause blanks, not numbers.

🧠 Conceptual

expert

Understanding When to Use Bar vs Column Charts

Which scenario best explains when a horizontal bar chart is more effective than a vertical column chart?

A When data values are very small and close to zero

B When showing trends over time with continuous dates

C When displaying percentages stacked to 100%

D When comparing many categories with long names that need to be fully visible

💡 Hint

Think about label readability and space.

Horizontal bar charts allow long category names to be displayed clearly on the vertical axis. Column charts are better for time series or stacked percentages. Small values do not determine orientation choice.