DA0-002 Visualization and Reporting Practice Test 5 — 15 Questions

Question 1

Which TWO color choices are appropriate for a categorical data visualization? (Select two.)

Accepted Answer

Distinct hues. Distinct hues (A) are appropriate for categorical data because they use different colors to represent distinct categories without implying any order or magnitude. This aligns with best practices in data visualization where categorical variables require qualitative color schemes that maximize perceptual separation between groups.

Answer

Sequential color scheme

Answer

Monochrome

Answer

Rainbow gradient

Question 2

Refer to the exhibit. A data analyst attempts to visualize the dataset but receives a permission error. The analyst's username is 'analyst2'. What is the most likely cause?

Accepted Answer

The analyst's username is not in the users list. The policy lists only 'admin' and 'analyst'; 'analyst2' is not included, so access is denied.

Answer

The analyst lacks write permission

Answer

The analyst lacks read permission

Answer

JSON syntax error prevents parsing

Question 3

Refer to the exhibit. What is the best corrective action to resolve this error?

Accepted Answer

Convert the 'revenue' column to numeric data type during ETL. The error indicates a data type mismatch; converting the column to numeric in the ETL process ensures compatibility.

Answer

Change the chart type to a bar chart

Answer

Remove the 'revenue' column from the visualization

Answer

Use a string-compatible chart type

Question 4

Refer to the exhibit. A stakeholder complains that the line chart exaggerates the changes in sales. What is the most likely cause?

Accepted Answer

The y-axis does not start at zero. Setting beginAtZero to false truncates the y-axis, making small changes appear larger.

Answer

There are too few data points

Answer

The data labels are incorrect

Answer

The chart type should be a bar chart

Question 5

A company needs to visualize monthly sales revenue for the past five years to identify seasonal trends. Which chart type is most appropriate?

Accepted Answer

Line chart with months on the x-axis and revenue on the y-axis. (Line chart) is correct because line charts are ideal for showing trends over time, such as monthly sales revenue over five years. Option B (Stacked bar chart) would segment each year, making it harder to see continuous trends. Option C (Scatter plot) is for relationships between two variables, not time series trends. Option D (Pie chart) is for proportions, not trends over time.

Answer

Stacked bar chart showing each year as a segment

Answer

Scatter plot with revenue vs. month number

Answer

Pie chart for each year showing revenue distribution

Question 6

A data analyst is designing a dashboard for executives. Which best practice should be followed regarding the placement of key performance indicators (KPIs)?

Accepted Answer

Place the most important KPIs in the top-left corner. Placing the most important KPIs in the top-left corner follows natural reading patterns (left-to-right, top-to-bottom), ensuring executives see critical metrics first. Option A is wrong because including too many charts on a single screen reduces readability and can overwhelm users. Option B is wrong because hiding KPIs behind filters defeats the purpose of having them visible at a glance. Option C is wrong because using distinct colors for all KPIs equally does not prioritize information; it may actually create visual clutter and reduce clarity.

Answer

Include as many charts as possible on a single screen to avoid scrolling

Answer

Hide KPIs behind filters to reduce initial load time

Answer

Use distinct colors to highlight all KPIs equally

Question 7

A data analyst creates a bar chart to compare average customer satisfaction scores across five departments. The chart shows very tall bars for three departments and very short bars for two departments, making differences hard to assess. What is the most likely cause and the best fix?

Accepted Answer

The y-axis does not start at zero; reset it to zero. A bar chart's y-axis should start at zero to accurately represent proportions; a truncated y-axis can exaggerate differences between bars. Option A is wrong because grouping by region does not address the axis scaling issue. Option C is wrong because a line chart is not suitable for comparing discrete categories. Option D is wrong because 3D charts can introduce visual distortion and are not recommended.

Answer

The chart should group departments by region to reduce variance

Answer

The data should be visualized as a line chart instead

Answer

The bar chart should be replaced with a 3D version for better depth

Question 8

A business analyst wants to compare the proportion of total sales contributed by each product category in the current year. Which visualization is most suitable?

Accepted Answer

Pie chart. A pie chart effectively shows the proportion of each category as part of a whole, which is ideal for comparing the contribution of each product category to total sales. Option B (scatter plot) is used to show relationships between two variables, not proportions. Option C (line chart) is best for trends over time. Option D (histogram) shows the distribution of a continuous variable.

Answer

Scatter plot

Answer

Line chart

Answer

Histogram

Question 9

An analyst creates a dashboard with multiple visualizations. Which feature allows users to change the data displayed across all charts simultaneously?

Accepted Answer

Filter or slicer. A filter or slicer can be applied to multiple visualizations on a dashboard, allowing users to change the data displayed across all charts simultaneously. Option A (Linked chart) is not a standard feature for simultaneous filtering. Option B (Drill-down) enables exploring hierarchical data but does not affect multiple charts at once. Option D (Data segmentation) typically refers to dividing data into groups, not a cross-chart filtering mechanism.

Answer

Linked chart

Answer

Drill-down

Answer

Data segmentation

Question 10

A data scientist creates a box plot of employee salaries and notices many outliers above the upper whisker. What action should be taken to best understand the salary distribution?

Accepted Answer

Investigate the outliers to determine if they are data entry errors or valid extremes. Investigating outliers is essential to understand the salary distribution. Outliers above the upper whisker may represent legitimate high salaries (e.g., executives) or data entry errors, and understanding their source guides appropriate analysis. Option A is wrong because a histogram does not inherently clarify the nature of outliers. Option B is wrong because blindly removing outliers can distort the true distribution. Option C is wrong because trimming top salaries without investigation loses valuable information and biases the analysis.

Answer

Replace the box plot with a histogram of the salaries

Answer

Remove all outliers to create a more typical box plot

Answer

Trim the top 5% of salaries and recreate the box plot

Question 11

A dashboard designer needs to ensure that color choices are accessible to users with color vision deficiencies. Which practice should be followed?

Accepted Answer

Combine color with patterns or labels to convey information. Combining color with patterns or labels ensures that information is conveyed even to users with color vision deficiencies. Option A is incorrect as rainbow palettes rely solely on hue differentiation, which is problematic for color-blind users. Option B is incorrect because converting to grayscale loses the emphasis that color can provide for all users. Option C is incorrect because red-green color schemes are particularly difficult for those with red-green color blindness (the most common type).

Answer

Use a rainbow color palette to maximize differentiation

Answer

Convert all charts to grayscale

Answer

Use red and green to indicate positive and negative values

Question 12

Which TWO actions will improve the readability of a bar chart showing quarterly sales across five regions?

Accepted Answer

Sort bars in descending order of sales. Sorting bars by sales (B) allows immediate comparison of relative performance. Adding data labels on each bar (C) provides exact figures, enhancing readability. Option A is incorrect because overlaying a line chart on a bar chart mixes data types and can obscure the bar values. Option D (vertical gridlines for every bar) often adds visual clutter without improving readability. Option E (3D bar chart) distorts perspective and makes accurate comparison difficult.

Answer

Overlay a line chart showing cumulative sales

Answer

Add vertical gridlines for every bar

Answer

Switch to a 3D bar chart to add visual depth

Question 13

Which THREE are best practices for designing a dashboard for executive consumption?

Accepted Answer

Ensure the dashboard is responsive for mobile devices. Options B, D, and E are correct. For executive dashboards, mobile responsiveness (B) ensures accessibility on any device, interactive filters (D) allow executives to drill down into time periods and regions, and displaying critical KPIs at the top (E) highlights key insights. Option A is incorrect because executives need summarized, not raw, data. Option C is incorrect because separate charts for each metric can create clutter; instead, use combined or summary visualizations.

Answer

Include detailed raw data tables for transparency

Answer

Use a separate chart for each metric to avoid clutter

Question 14

Which TWO are common mistakes when creating data visualizations?

Accepted Answer

Using excessive 3D effects that obscure data. Options A and E are correct. Excessive 3D effects (A) can obscure or distort data, and truncating the y-axis (E) can mislead viewers by exaggerating differences. Option B is incorrect because sorting alphabetically can aid lookup, not a mistake. Option C is incorrect because color-blind friendly palettes are a best practice, not a mistake. Option D is incorrect because including a legend is standard good practice.

Answer

Sorting categories alphabetically in a bar chart

Answer

Choosing a color-blind friendly palette

Answer

Including a legend to identify chart elements

Question 15

A retail company with 500 stores across North America wants to visualize its sales performance. The dataset includes store ID, region (Northeast, Southeast, Midwest, West), product category (Electronics, Clothing, Home Goods), monthly sales (in dollars), and date (from January 2018 to December 2023). The data has missing values for about 5% of store-month combinations, and a few stores have reported sales that are 10 times higher than the average for their region due to grand opening events. The goal is to create a dashboard that shows monthly sales trends for each region and product category, and allows users to identify which categories are driving growth. Which approach should the analyst take?

Accepted Answer

Create a line chart with month on the x-axis, sales on the y-axis, and separate lines for each region and category; check for outliers and consider annotating them. A line chart with month on the x-axis and separate lines for each region and product category allows clear visualization of sales trends over time for each combination. This approach also suggests checking for outliers (e.g., grand opening spikes) and annotating them rather than removing them, preserving valuable data. Option A is incorrect: a stacked bar chart with all stores aggregated per month obscures individual category and regional trends, making it hard to identify which categories are driving growth. Option C is incorrect: removing outlier stores discards legitimate data from grand opening events, which may be important for understanding growth. Option D is incorrect: a scatter plot is not suitable for showing time series trends as it does not connect points sequentially and makes trend identification difficult.

Answer

Use a stacked bar chart showing total sales by month, with each bar segmented by region and category

Answer

Create a scatter plot of sales vs. month with dots colored by region

Answer

Remove all stores with outlier sales and then create a line chart of the cleansed data