Visualizing Data practice questions

A data analyst is creating a dashboard to monitor server CPU utilization over the past 24 hours. Which chart type is most appropriate for showing the trend of CPU usage over time?

Trap 1: Scatter plot

Scatter plots show correlation between two variables, not time series.

Trap 2: Pie chart

Pie charts are for parts of a whole, not time trends.

Trap 3: Bar chart

Bar charts compare categories, not continuous trends.

A
Scatter plot
Why wrong: Scatter plots show correlation between two variables, not time series.
B
Pie chart
Why wrong: Pie charts are for parts of a whole, not time trends.
C
Line chart
Line charts display trends over time effectively.
D
Bar chart
Why wrong: Bar charts compare categories, not continuous trends.

Question 2mediummultiple choice

A sales dashboard shows monthly revenue but the bars are very tall for some months and very short for others, making comparisons difficult. Which visualization modification would best improve readability?

Trap 1: Change bar colors to gradient

Color changes don't address scale distortion.

Trap 2: Add more horizontal gridlines

Gridlines improve reading but don't fix the scale issue.

Trap 3: Use a 3D bar chart for depth

3D can distort perception and makes comparison harder.

A
Change bar colors to gradient
Why wrong: Color changes don't address scale distortion.
B
Apply a logarithmic scale on the y-axis
Log scale compresses wide ranges so differences are visible.
C
Add more horizontal gridlines
Why wrong: Gridlines improve reading but don't fix the scale issue.
D
Use a 3D bar chart for depth
Why wrong: 3D can distort perception and makes comparison harder.

Question 3hardmultiple choice

A data analyst creates a bubble chart showing country GDP (x-axis), life expectancy (y-axis), and population (bubble size). However, large bubbles overlap and obscure many data points. Which corrective action should the analyst take?

Trap 1: Increase the chart canvas size

Larger canvas doesn't prevent overlap if bubbles are large.

Trap 2: Reduce all bubble sizes uniformly

Smaller bubbles may make small populations invisible.

Trap 3: Remove outlier countries with large populations

Removing data is not recommended; skews analysis.

A
Increase the chart canvas size
Why wrong: Larger canvas doesn't prevent overlap if bubbles are large.
B
Set bubble opacity to 70%
Transparency allows seeing through overlapping bubbles.
C
Reduce all bubble sizes uniformly
Why wrong: Smaller bubbles may make small populations invisible.
D
Remove outlier countries with large populations
Why wrong: Removing data is not recommended; skews analysis.

Question 4easymultiple choice

An analyst wants to show the distribution of test scores for 500 students. Which visualization type is best for understanding the shape of the distribution?

Trap 1: Line chart

Line charts show trends over time.

Trap 2: Pie chart

Pie charts show proportions, not distribution.

Trap 3: Scatter plot

Scatter plots show relationship between two variables.

A
Line chart
Why wrong: Line charts show trends over time.
B
Pie chart
Why wrong: Pie charts show proportions, not distribution.
C
Scatter plot
Why wrong: Scatter plots show relationship between two variables.
D
Histogram
Histograms display frequency distribution of numerical data.

Question 5mediummultiple choice

A dashboard shows sales by region using a map with color intensity. Users complain that two regions with very different sales appear nearly the same color. What is the most likely cause?

Trap 1: The map projection is distorted

Projection affects area shape, not color encoding.

Trap 2: The monitor resolution is too low

Resolution affects sharpness, not color differentiation.

Trap 3: Users are color blind

While possible, the issue is more likely the color scale design.

A
The map projection is distorted
Why wrong: Projection affects area shape, not color encoding.
B
The color scale uses a sequential palette with insufficient contrast
Sequential palettes can have low perceptual difference between adjacent values.
C
The monitor resolution is too low
Why wrong: Resolution affects sharpness, not color differentiation.
D
Users are color blind
Why wrong: While possible, the issue is more likely the color scale design.

Question 6hardmultiple choice

An analyst creates a stacked bar chart showing quarterly sales by product category. The chart becomes hard to read because some categories have very small contributions. Which redesign is most effective?

Trap 1: Change to a pie chart for each quarter

Pie charts also struggle with small slices.

Trap 2: Increase the width of each bar

Wider bars don't make small segments more readable.

Trap 3: Switch to a 3D stacked column chart

3D makes comparison harder, especially for small segments.

A
Combine small categories into an 'Other' group
Grouping small items simplifies the chart and improves readability.
B
Change to a pie chart for each quarter
Why wrong: Pie charts also struggle with small slices.
C
Increase the width of each bar
Why wrong: Wider bars don't make small segments more readable.
D
Switch to a 3D stacked column chart
Why wrong: 3D makes comparison harder, especially for small segments.

Question 7easymultiple choice

A data analyst needs to present the correlation between advertising spend and website traffic. Which chart type is most appropriate?

Trap 1: Bar chart

Bar charts compare discrete categories, not correlation.

Trap 2: Line chart

Line charts are for time series trends.

Trap 3: Pie chart

Pie charts show parts of a whole.

A
Bar chart
Why wrong: Bar charts compare discrete categories, not correlation.
B
Line chart
Why wrong: Line charts are for time series trends.
C
Pie chart
Why wrong: Pie charts show parts of a whole.
D
Scatter plot
Scatter plots reveal relationships between two variables.

Question 8mediummultiple choice

An analyst builds a dashboard with a gauge showing 'Current Inventory Level' as a percentage. Stakeholders find the gauge misleading because it always shows near 100% even when inventory is low. What is the most likely issue?

Trap 1: The gauge updates too slowly

Update speed doesn't cause always-high readings.

Trap 2: The gauge uses green for all values

Color choice doesn't change the percentage calculation.

Trap 3: The gauge needle is too small

Needle size doesn't affect the reading accuracy.

A
The maximum value of the gauge is set too high
If max is high, actual value appears as a smaller percentage than reality.
B
The gauge updates too slowly
Why wrong: Update speed doesn't cause always-high readings.
C
The gauge uses green for all values
Why wrong: Color choice doesn't change the percentage calculation.
D
The gauge needle is too small
Why wrong: Needle size doesn't affect the reading accuracy.

Question 9hardmultiple choice

A data analyst creates a heatmap to show website click-through rates by hour and day of week. The heatmap uses a green-to-red gradient, but users cannot distinguish between moderate values. What is the best fix?

Trap 1: Remove all but the highest and lowest values

Removing data discards information.

Trap 2: Add black borders around each cell

Borders can create visual noise without improving color distinction.

Trap 3: Increase the size of each heatmap cell

Cell size doesn't improve color perception.

A
Switch to a diverging color scheme with a neutral center
Diverging palettes highlight midpoints and differentiate values better.
B
Remove all but the highest and lowest values
Why wrong: Removing data discards information.
C
Add black borders around each cell
Why wrong: Borders can create visual noise without improving color distinction.
D
Increase the size of each heatmap cell
Why wrong: Cell size doesn't improve color perception.

Question 10mediummulti select

Which TWO actions are best practices for creating effective data visualizations?

Trap 1: Maximize data-ink ratio by removing all whitespace

Whitespace improves readability; removing all is counterproductive.

Trap 2: Always include gridlines with high contrast

Gridlines should be subtle so they don't distract.

Trap 3: Use 3D effects to make charts look professional

3D can distort data and hinder accurate reading.

A
Avoid using more than five slices in a pie chart
Too many slices make pie charts unreadable.
B
Maximize data-ink ratio by removing all whitespace
Why wrong: Whitespace improves readability; removing all is counterproductive.
C
Always include gridlines with high contrast
Why wrong: Gridlines should be subtle so they don't distract.
D
Use 3D effects to make charts look professional
Why wrong: 3D can distort data and hinder accurate reading.
E
Use color to represent data values consistently
Color coding helps convey information efficiently.

Question 11hardmulti select

Which THREE factors should be considered when choosing a chart type for a dataset?

Trap 1: The animation capabilities of the software

Animation is not a primary consideration for chart selection.

Trap 2: The color scheme of the company logo

Color scheme is aesthetic, not a deciding factor for chart type.

A
The animation capabilities of the software
Why wrong: Animation is not a primary consideration for chart selection.
B
The data types (categorical, numerical, time series)
Data type determines suitable chart types.
C
The number of variables to display
More variables may require advanced charts like bubble or multi-line.
D
The key insight or message to convey
The chart should highlight the intended message.
E
The color scheme of the company logo
Why wrong: Color scheme is aesthetic, not a deciding factor for chart type.

Question 12easymulti select

Which TWO visualization types are suitable for showing the distribution of a single continuous variable?

Trap 1: Line chart

Line charts show trends over time.

Trap 2: Scatter plot

Scatter plots show relationship between two continuous variables.

Trap 3: Pie chart

Pie charts show proportions of categorical data.

A
Box plot
Box plots display summary statistics and distribution shape.
B
Line chart
Why wrong: Line charts show trends over time.
C
Histogram
Histograms show frequency distribution of continuous data.
D
Scatter plot
Why wrong: Scatter plots show relationship between two continuous variables.
E
Pie chart
Why wrong: Pie charts show proportions of categorical data.

Read the full NAT/PAT explanation →

Question 13hardmultiple choice

You are a data analyst for an e-commerce company. Your team has built a dashboard to monitor daily sales performance across five regions: North, South, East, West, and Central. The dashboard includes a bar chart showing total sales per region, a line chart showing daily sales trend over the past 30 days, and a pie chart showing sales distribution by product category (Clothing, Electronics, Home, Books, Sports). Recently, stakeholders have complained that the pie chart is hard to interpret because the Sports category has very small sales and is barely visible. Also, the bar chart uses a rainbow color scheme that makes it difficult to compare bar heights because the colors are not ordered by magnitude. The line chart is fine. You need to redesign the dashboard to address these issues. Which combination of changes is most appropriate?

Trap 1: Replace the pie chart with a stacked bar chart and use a…

Stacked bar shows composition over time, not needed; categorical colors don't reflect magnitude.

Trap 2: Explode the Sports slice in the pie chart and use a monochromatic…

Exploding still shows small slice; monochromatic may not differentiate regions.

Trap 3: Change the pie chart to a 3D pie chart and use a diverging color…

3D pie distorts perception; diverging colors not ideal for ranking.

A
Replace the pie chart with a stacked bar chart and use a categorical color scheme for the bar chart
Why wrong: Stacked bar shows composition over time, not needed; categorical colors don't reflect magnitude.
B
Explode the Sports slice in the pie chart and use a monochromatic color scheme for the bar chart
Why wrong: Exploding still shows small slice; monochromatic may not differentiate regions.
C
Change the pie chart to a 3D pie chart and use a diverging color scheme for the bar chart
Why wrong: 3D pie distorts perception; diverging colors not ideal for ranking.
D
Group small categories into an 'Other' slice in the pie chart and use a sequential color scheme ordered by sales for the bar chart
Grouping small categories improves pie readability; sequential colors ordered by magnitude helps bar comparison.

Read the full NAT/PAT explanation →

Question 14mediummultiple choice

You are a data analyst at a logistics company. You have created a dashboard to monitor delivery performance. The dashboard includes a scatter plot showing delivery time (hours) vs. distance (miles) for each delivery, with points colored by delivery region (A, B, C, D, E). Users have reported that the scatter plot is cluttered because there are over 10,000 points, making it hard to see patterns. Additionally, the color legend for the five regions uses similar shades of blue, making it difficult to distinguish which region a point belongs to. You need to improve the scatter plot to reduce overplotting and improve region differentiation. Which approach is most effective?

Trap 1: Use a hexagonal binning plot (hexbin) with color representing region

Hexbin shows density but loses individual points; region coloring in hexbin is complex.

Trap 2: Create five separate scatter plots (small multiples) for each region

Small multiples reduce clutter but lose ability to see overall pattern and compare across regions.

Trap 3: Convert to a bubble chart by adding package weight as bubble size

Bubble chart still has overplotting and adds complexity.

A
Use a hexagonal binning plot (hexbin) with color representing region
Why wrong: Hexbin shows density but loses individual points; region coloring in hexbin is complex.
B
Create five separate scatter plots (small multiples) for each region
Why wrong: Small multiples reduce clutter but lose ability to see overall pattern and compare across regions.
C
Set point opacity to 30% and use a qualitative color palette with distinct hues for each region
Alpha blending reveals density; distinct colors improve region identification.
D
Convert to a bubble chart by adding package weight as bubble size
Why wrong: Bubble chart still has overplotting and adds complexity.

Question 15easymultiple choice

A data analyst needs to visualize the relationship between two continuous variables: advertising spend (in dollars) and monthly sales (in units). Which chart type is most appropriate?

Trap 1: Pie chart

Pie charts show parts of a whole, not relationships between two continuous variables.

Trap 2: Bar chart

Bar charts compare categories, not continuous variables.

Trap 3: Line chart

Line charts are best for trends over time; scatter plot is more direct for relationship analysis.

A
Scatter plot
Scatter plots are used to display the relationship between two continuous variables.
B
Pie chart
Why wrong: Pie charts show parts of a whole, not relationships between two continuous variables.
C
Bar chart
Why wrong: Bar charts compare categories, not continuous variables.
D
Line chart
Why wrong: Line charts are best for trends over time; scatter plot is more direct for relationship analysis.

Question 16mediummulti select

A data analyst is creating a dashboard for a retail company. The dashboard should provide insights into sales performance across multiple dimensions. Which TWO chart types are best suited for showing the contribution of each product category to total sales?

Trap 1: Scatter plot

Scatter plots show correlation, not part-to-whole relationships.

Trap 2: Histogram

Histograms show frequency distribution of a single variable.

Trap 3: Line chart

Line charts show trends over time, not contributions to a total.

A
Scatter plot
Why wrong: Scatter plots show correlation, not part-to-whole relationships.
B
Histogram
Why wrong: Histograms show frequency distribution of a single variable.
C
Pie chart
Pie charts effectively show each category's proportion of the total.
D
Line chart
Why wrong: Line charts show trends over time, not contributions to a total.
E
Stacked bar chart
Stacked bar charts can show the composition of each category relative to the whole.

Review the full routing breakdown →

Question 17hardmultiple choice

You are a data analyst for a logistics company. The company has a fleet of delivery trucks and tracks performance metrics including delivery time, fuel consumption, and distance traveled. Management wants a dashboard to monitor driver efficiency and identify underperforming drivers. You have access to a dataset with columns: DriverID, Date, RouteID, Distance (miles), FuelUsed (gallons), DeliveryTime (minutes). The dataset contains 10,000 records from the past year. You need to create a visualization that allows management to quickly compare the average fuel efficiency (miles per gallon) of drivers and also see how consistent each driver's efficiency is. Which of the following approaches is the best course of action?

Trap 1: Create a line chart with Date on the x-axis and MPG on the y-axis,…

A line chart with many drivers would be too cluttered and does not efficiently compare distributions.

Trap 2: Create a scatter plot with Distance on the x-axis and FuelUsed on…

A scatter plot with 10,000 points would be cluttered and not clearly show per-driver efficiency or consistency.

Trap 3: Create a bar chart showing the average MPG for each driver.

A bar chart shows only averages, not variability; it does not indicate consistency.

A
Create a line chart with Date on the x-axis and MPG on the y-axis, with separate lines for each driver.
Why wrong: A line chart with many drivers would be too cluttered and does not efficiently compare distributions.
B
Create a box plot grouped by DriverID showing the distribution of MPG for each driver.
A box plot shows median, quartiles, and outliers, allowing comparison of both central tendency and variability.
C
Create a scatter plot with Distance on the x-axis and FuelUsed on the y-axis, color-coded by DriverID.
Why wrong: A scatter plot with 10,000 points would be cluttered and not clearly show per-driver efficiency or consistency.
D
Create a bar chart showing the average MPG for each driver.
Why wrong: A bar chart shows only averages, not variability; it does not indicate consistency.

Question 18mediumdrag order

Drag and drop the steps to clean a dataset with missing values in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 19mediumdrag order

Drag and drop the steps to resolve data integration conflicts in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 20mediummatching