Scatter plots in Tableau - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Identify the correct scatter plot setup

You want to create a scatter plot in Tableau to analyze the relationship between Sales and Profit for different Regions. Which of the following setups will correctly create a scatter plot showing each region as a point?

A Drag Profit to Columns, Sales to Rows, and Region to Tooltip.

B Drag Region to Columns, Sales to Rows, and Profit to Color on the Marks card.

C Drag Sales to Rows, Profit to Columns, and Region to Filters.

D Drag Sales to Columns, Profit to Rows, and Region to Detail on the Marks card.

💡 Hint

Remember, for a scatter plot, you need two numeric measures on the axes and a dimension to identify points.

Option D correctly places Sales and Profit on the axes and uses Region on Detail to show each region as a separate point. Other options either misplace dimensions or use filters/tooltips which do not create distinct points.

❓ dax_lod_result

intermediate

Calculate average sales per customer for scatter plot

You want to create a scatter plot showing Customer Count on X-axis and Average Sales per Customer on Y-axis. Which DAX measure correctly calculates the average sales per customer?

Tableau

Average Sales per Customer = DIVIDE(SUM(Sales[SalesAmount]), DISTINCTCOUNT(Sales[CustomerID]))

A Average Sales per Customer = DIVIDE(SUM(Sales[SalesAmount]), DISTINCTCOUNT(Sales[CustomerID]))

B Average Sales per Customer = SUM(Sales[SalesAmount]) / COUNT(Sales[CustomerID])

C Average Sales per Customer = AVERAGE(Sales[SalesAmount]) / DISTINCTCOUNT(Sales[CustomerID])

D Average Sales per Customer = SUM(Sales[SalesAmount]) * DISTINCTCOUNT(Sales[CustomerID])

💡 Hint

Use DIVIDE to safely handle division and DISTINCTCOUNT to count unique customers.

Option A correctly divides total sales by the distinct number of customers. Option A uses COUNT which counts all rows, not unique customers. Option A divides average sales by distinct customers, which is incorrect. Option A multiplies instead of dividing.

🧠 Conceptual

advanced

Best practice for color encoding in scatter plots

When adding color to a scatter plot to represent a categorical variable with many categories, what is the best practice to ensure clarity and accessibility?

A Use a distinct color for each category regardless of the number of categories.

B Limit the number of categories shown by grouping less frequent ones into an 'Other' category and use a colorblind-friendly palette.

C Use random colors for each category to make the plot colorful and engaging.

D Use only two colors alternating for all categories to keep it simple.

💡 Hint

Think about how too many colors can confuse viewers and accessibility for colorblind users.

Option B is best because it reduces clutter by grouping minor categories and uses a palette accessible to colorblind users. Option B can overwhelm and confuse. Option B lacks consistency and accessibility. Option B oversimplifies and misrepresents categories.

🔧 Formula Fix

advanced

Why does the scatter plot show overlapping points?

You created a scatter plot with Sales on X-axis and Profit on Y-axis, but many points overlap making it hard to see individual data points. What is the best way to fix this in Tableau?

A Add jitter by creating calculated fields that add small random noise to Sales and Profit values.

B Change the mark type from Circle to Line.

C Remove the dimension from the Detail card to reduce the number of points.

D Increase the size of the points to make them more visible.

💡 Hint

Think about how to separate points that have the same coordinates.

Option A adds jitter to spread overlapping points slightly, improving visibility. Option A makes points bigger but does not separate overlaps. Option A reduces points but loses detail. Option A changes the plot type incorrectly.

🎯 Scenario

expert

Designing a scatter plot dashboard for sales performance

You are designing a dashboard with a scatter plot showing Sales vs Profit Margin by Product Category. You want to allow users to filter by Region and Year. Which approach ensures the filters correctly update the scatter plot without performance issues?

A Create separate scatter plots for each Region and Year combination and use dashboard actions to switch between them.

B Apply Region and Year filters directly on the scatter plot without context filters to keep dashboard simple.

C Use context filters for Region and Year before loading the scatter plot to reduce data volume and improve performance.

D Load all data into the scatter plot and use quick filters for Region and Year without any optimization.

💡 Hint

Think about how Tableau processes filters and how to optimize large datasets.

Option C uses context filters to limit data early, improving performance. Option C can cause slow loading with large data. Option C is complex and inefficient. Option C risks performance issues by loading all data.