CCNA Visualizing Data Questions — Page 2 of 2

MCQhard

A hospital's analytics team has created a dashboard for tracking patient wait times across departments. The dashboard uses a stacked bar chart showing average wait time per department, with each bar segmented by severity level (Low, Medium, High). However, management complains that it is difficult to compare total wait times across departments or identify which department has the highest average wait time. The data itself is accurate and complete. The analyst needs to redesign the visualization to address these concerns. Which course of action should the analyst take?

A.Replace the stacked bar chart with multiple line charts, one per department.

B.Use a heat map with departments on one axis and severity levels on the other, with color intensity representing wait time.

C.Change the chart to a grouped bar chart, with each department having separate bars for each severity level placed side-by-side.

D.Switch to a pie chart showing the proportion of total wait time each department contributes.

AnswerC

This allows direct comparison of totals and individual segments across departments.

Why this answer

Option A is correct. A grouped bar chart with bars for each department and separate bars for each severity level side-by-side allows direct comparison of total wait times across departments and easy identification of the department with the highest average wait time. Option B is incorrect because line charts are not suitable for categorical comparisons and would clutter the view with many lines.

Option C is incorrect because a pie chart can only show proportions for a single category (e.g., total wait time per department) and does not convey severity levels. Option D is incorrect because a heat map shows patterns but does not clearly compare total wait times across departments; it is harder to read exact values.

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MCQmedium

An analyst is creating a report that includes multiple charts. To ensure the audience quickly grasps the key insight, which principle of data storytelling should be applied?

A.Avoid using titles to reduce clutter

B.Include a legend for every chart

C.Use a title that states the main insight

D.Place the chart before any explanation

AnswerC

A title that summarizes the finding helps the audience understand the message immediately.

Why this answer

Starting with a clear title that summarizes the takeaway guides the audience to the main insight. Using a legend only or placing the chart first does not provide context. Avoiding titles reduces clarity.

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Matchingmedium

Match each data type to its example.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Country of origin (e.g., USA, Canada)

Customer satisfaction rating (1-5)

Temperature in Celsius

Annual income in dollars

Gender (Male, Female, Other)

Why these pairings

These are fundamental data types in statistics.

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MCQhard

A data scientist has a dataset with 50 variables and wants to identify clusters of similar observations. Which visualization technique is most suitable for reducing dimensionality to 2D while preserving cluster structure?

A.Heatmap of correlations

B.Scatter matrix (pairplot)

C.Parallel coordinates plot

D.Scatter plot of first two principal components

AnswerD

PCA reduces dimensions while preserving variance, enabling visualization of clusters.

Why this answer

Option C is correct because PCA (or t-SNE) is designed for dimensionality reduction to visualize clusters. Other options are less effective for high-dimensional data.

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MCQhard

An analyst creates a histogram of customer transaction amounts but observes that the distribution looks bimodal. Upon review, the analyst realizes that two different customer segments (retail and wholesale) were combined. Which action best addresses this?

A.Create two separate histograms, one for each segment

B.Use a single histogram with two colors for segments

C.Use a box plot instead of a histogram

D.Increase the number of bins to see more detail

AnswerA

Separate histograms clearly show each segment's distribution and the reason for bimodality.

Why this answer

Splitting the data into two separate histograms by segment reveals the distinct distributions. Changing bin width may hide or exaggerate features. Combining into one chart with color is possible but may still obscure the bimodal nature if overlapping.

A box plot shows medians but not shape.

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MCQmedium

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?

A.Use a hexagonal binning plot (hexbin) with color representing region

B.Create five separate scatter plots (small multiples) for each region

C.Set point opacity to 30% and use a qualitative color palette with distinct hues for each region

D.Convert to a bubble chart by adding package weight as bubble size

AnswerC

Alpha blending reveals density; distinct colors improve region identification.

Why this answer

Option C is correct because reducing opacity (alpha blending) mitigates overplotting by making overlapping points more transparent, while switching to a qualitative color palette (e.g., distinct hues like red, green, blue) ensures each of the five regions is easily distinguishable. This directly addresses both user complaints without losing the overall distribution context.

Exam trap

The trap here is that candidates often choose small multiples (Option B) thinking they reduce clutter, but the question specifically asks to improve differentiation and reduce overplotting in a single view, and small multiples fragment the data, making cross-region comparison harder.

How to eliminate wrong answers

Option A is wrong because a hexagonal binning plot aggregates points into hexagons based on density, but it cannot encode a categorical variable like region with color inside a single plot—hexbin typically uses a single color gradient for density, not multiple regions. Option B is wrong because creating five separate scatter plots (small multiples) forces users to mentally compare across separate charts, losing the ability to see relative spatial patterns and interactions between regions in a single view. Option D is wrong because converting to a bubble chart by adding package weight as bubble size does not reduce overplotting (it can worsen it with larger circles) and does not improve region differentiation; it introduces a third continuous variable that is not related to the reported issues.

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MCQhard

A dataset contains salaries ranging from $25,000 to $2,500,000, with most salaries under $100,000. Which chart type best shows the distribution without distortion from extreme values?

A.Box plot

B.Scatter plot

C.Histogram with equal bin widths

D.Line chart

AnswerA

Box plots show median, quartiles, and outliers, handling skew well.

Why this answer

Option B is correct because box plots display distribution with outliers clearly separated, unaffected by extreme values in terms of scale. Histograms may compress the bulk of data.

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MCQeasy

A retail company operates 50 stores across the country. The data analyst has been asked to create a dashboard to visualize monthly sales trends over the past two years and compare the performance of the top 5 stores. The dataset includes store name, date, and daily sales amount. Initial exploration reveals that some stores have missing sales data for certain months due to system outages, and there are occasional extreme values caused by promotional events (e.g., Black Friday sales are 10x normal). The analyst needs to choose an appropriate visualization approach that accurately represents the trends and comparisons while handling these data quality issues. What should the analyst do to best meet the requirements?

A.Use a line chart for each store's monthly sales and exclude outliers from the dataset.

B.Use a bar chart showing monthly sales for each month and filter out stores with missing data.

C.Use a line chart for the overall monthly sales trend (aggregated across all stores) and a separate bar chart for the top 5 stores by total sales, with tooltips explaining outlier values.

D.Use a stacked bar chart with all 50 stores, each bar representing a month.

AnswerC

Combines trend and comparison effectively; aggregation handles missing data, and tooltips provide context.

Why this answer

Option C is correct because it combines a line chart for overall trend (aggregating all stores) and bar charts for top 5 stores, which allows comparison while handling missing data through aggregation. Tooltips can provide context for outliers. Option A is wrong because using separate line charts for each store becomes cluttered with 50 stores, and excluding outliers may omit valuable information about promotions.

Option B is wrong because filtering out stores with missing data could remove important stores and distort the trend. Option D is wrong because a stacked bar chart with all 50 stores would be difficult to read and doesn't show trends over time effectively.

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Drag & Dropmedium

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

Why this order

Cleaning starts with identification, then analysis of missingness, strategy selection, application, and documentation.

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