DA0-002 Visualization and Reporting Practice Test 6 — 15 Questions

Question 1

An IT operations team monitors 200 servers. Each server reports CPU utilization (0-100%) every five minutes for the past year. The team wants to visualize the data to identify servers that are consistently over 80% utilization and detect any unusual spikes. They have a large dataset with 100,000+ records per server. The current visualization is a single scatter plot with CPU utilization on the y-axis, time on the x-axis, and each server as a different colored point. The chart is extremely cluttered, with points overlapping and colors indistinguishable. What should the team do to improve the visualization?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use a heatmap showing CPU utilization over time per server, or create small multiple charts (one per server). Heatmaps and small multiples (trellis charts) are effective for visualizing large, dense datasets with multiple categories. A heatmap can show CPU utilization intensity over time for all servers in a compact form, making it easy to identify consistently high utilization and spikes. Small multiples create separate charts per server, avoiding overlap and allowing comparison. Option B is wrong because a single line chart with 200 lines would be even more cluttered than the scatter plot, making it impossible to distinguish individual servers. Option C is wrong because trend lines remove the individual data points needed to detect unusual spikes, and they would not show the actual utilization values. Option D is wrong because increasing the size of data points would worsen the overlap and clutter, making the chart even less readable.

Answer

Switch to a line chart with each server as a separate line

Answer

Add a trend line to each server's data and remove the individual points

Answer

Increase the size of the data points to make them more visible

Question 2

A marketing team conducted a customer satisfaction survey for five different departments (Sales, Support, Billing, Shipping, Returns). The survey asked customers to rate their satisfaction on a scale of 1 (Very Dissatisfied) to 5 (Very Satisfied). The data is ordinal and the team wants to visualize the distribution of responses for each department to quickly see which department has the most 'Very Satisfied' customers and which has the most 'Very Dissatisfied'. They also want to compare the spread of responses across departments. Which chart type should they use?

Accepted Answer

Stacked bar chart with departments on x-axis and counts of each rating stacked. The stacked bar chart, is correct because it displays the distribution of ordinal satisfaction ratings for each department, allowing easy comparison of counts for each rating category. With departments on the x-axis and stacked segments for ratings 1-5, the team can quickly identify which department has the most 'Very Satisfied' (rating 5) and 'Very Dissatisfied' (rating 1). The stacked bars also reveal the spread of responses across departments. Option C (box plot) is less suitable because it assumes continuous interval data and does not show exact counts of each ordinal category. Option B (line chart) is for trends over a continuous axis, not categorical comparisons of distributions. Option D (scatter plot) is for relationships between two continuous variables, not for comparing response distributions.

Answer

Line chart with departments on x-axis and average rating on y-axis

Answer

Box plot for each department

Answer

Scatter plot with department as category and satisfaction score as value

Question 3

A finance department wants to compare actual expenses against budgeted amounts for each of 10 departments over the past 12 months. They need to show the variance (over/under budget) as well as the trend of expenses over time. The data includes monthly actual and budget figures for each department. The audience is the CFO who needs a quick overview. Which visualization approach should be used?

Accepted Answer

A dual-axis chart with bars for actual and budget by month for each department, and a line for variance. A dual-axis chart with bars for actual and budget by month for each department, and a line for variance, allows the CFO to quickly compare actual vs budget figures and see the trend of variance over time. Option B (waterfall chart) is better suited for cumulative changes over time, not month-by-month comparisons. Option C (table with conditional formatting) does not provide a quick visual overview of trends. Option D (stacked bar chart) makes it difficult to compare actual vs budget across departments and months, and obscures individual department trends.

Answer

A waterfall chart showing cumulative variance over the year

Answer

A table with conditional formatting to highlight over-budget cells

Answer

Stacked bar chart with one bar per month, segments showing each department's actual and budget

Question 4

A data analyst is creating a dashboard to display monthly sales trends for the past two years. The dataset includes monthly sales figures with seasonal fluctuations. The analyst wants to highlight both the overall trend and the seasonal patterns effectively. Which TWO chart types are most appropriate for this purpose? (Select two.)

Accepted Answer

Line chart. A line chart (A) clearly shows the overall trend over time, while a bar chart (D) allows easy comparison of monthly values, revealing seasonal peaks and troughs. A stacked area chart (B) can show cumulative trends but makes individual monthly comparisons difficult. A scatter plot (C) is for correlations, not time series. A pie chart (E) is for parts of a whole and not suitable for trends.

Answer

Stacked area chart

Answer

Scatter plot

Answer

Pie chart

Question 5

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?

Accepted Answer

Change the chart to a grouped bar chart, with each department having separate bars for each severity level placed side-by-side.. A grouped bar chart with each department showing separate bars for each severity level placed side-by-side enables direct comparison of total wait times across departments and easy identification of the department with the highest average wait time. Option A is incorrect because multiple line charts are designed for time-series data, not for comparing categorical totals across departments, and would clutter the view. Option B is incorrect because a heat map can show patterns but does not allow clear comparison of total wait times or identification of the highest average. Option D is incorrect because a pie chart only shows proportions of a single category (e.g., total wait time per department) and loses severity-level information, making it impossible to compare total wait times meaningfully.

Answer

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

Answer

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

Answer

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

Question 6

A retail company has collected data on monthly advertising spend (in thousands of dollars) and corresponding sales (in thousands of dollars) over the past 12 months. The analyst creates a scatter plot to visualize the relationship between advertising spend and sales. The plot shows a cluster of points with a positive trend, but there is one extreme outlier where spend was $100,000 but sales were only $20,000. Upon investigation, the analyst discovers that the outlier is due to a data entry error: the sales figure should have been $200,000. The analyst wants to present the overall trend accurately in a meeting. Which course of action should the analyst take first?

Accepted Answer

Remove the outlier and recreate the scatter plot.. Since the outlier is due to a data entry error, the appropriate first step is to correct the data (change the sales figure to $200,000) or remove the outlier, then recreate the scatter plot. This ensures the visualization accurately reflects the true relationship. Option A is incorrect: a bar chart with binned averages would still be affected by the erroneous data point and would not show the individual relationship clearly. Option B is incorrect: adding a trend line to the current plot would still be influenced by the erroneous outlier, potentially distorting the trend. Option D is incorrect: a line chart is typically used for time series data, and this is not a time series; moreover, it would still include the outlier.

Answer

Use a bar chart to show average sales per advertising spend bin.

Answer

Add a trend line using linear regression to the current scatter plot.

Answer

Change the chart type to a line chart.

Question 7

A data analyst needs to visualize the relationship between two continuous variables, such as sales revenue and advertising spend, to identify potential correlation. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is specifically designed to show the relationship between two continuous variables and can reveal correlations, clusters, or outliers. Option A is wrong because pie charts show proportions of a whole, not relationships between two continuous variables. Option B is wrong because line charts show trends over time, not correlation between two variables. Option C is wrong because bar charts compare discrete categories, not continuous relationships.

Answer

Pie chart

Answer

Line chart

Answer

Bar chart

Question 8

A data analyst is troubleshooting a map visualization that shows null values for some regions. Which TWO actions should the analyst take to resolve the issue?

Accepted Answer

Check that latitude and longitude fields are properly geocoded.. Improper geocoding of latitude and longitude fields can result in null values on a map. Option B is correct because missing region names in the data source cause nulls; verifying and correcting the data source resolves the issue. Option C is incorrect because filtering out nulls hides the problem without fixing the underlying data. Option D is incorrect because changing the mark type does not address missing data. Option E is incorrect because removing color encoding only alters the visual appearance, not the data integrity.

Answer

Add a filter to exclude null values.

Answer

Change the mark type from Map to Pie.

Answer

Remove the color encoding from profit.

Question 9

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?

Accepted Answer

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.. 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.

Answer

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

Answer

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

Answer

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

Question 10

A data analyst wants to compare the total sales of four different product categories for a single year. Which chart type is most appropriate for this comparison?

Accepted Answer

Bar chart. A bar chart is ideal for comparing discrete categories because the length of each bar represents the total value, making it easy to see which category has the highest sales.

Answer

Pie chart

Answer

Scatter plot

Answer

Line chart

Question 11

A dashboard designer is creating a sales performance dashboard and wants to minimize non-data ink to improve clarity. Which action best follows the principle of maximizing the data-ink ratio?

Accepted Answer

Removing gridlines that do not add value to the chart. It directly follows Tufte's data-ink ratio principle by removing non-data ink (gridlines that don't aid interpretation) while preserving the core data. This maximizes the proportion of ink devoted to actual sales metrics, improving clarity without sacrificing information.

Answer

Including a detailed company logo on every chart

Answer

Adding a background image to make the dashboard more visually appealing

Answer

Using 3-D effects on bars to make them stand out

Question 12

An analyst has a dataset with monthly sales figures for the past five years. They want to highlight the cumulative effect of sales over time and show the total contribution of each month stacked on top of previous months. Which chart type is most suitable?

Accepted Answer

Area chart. An area chart is the most suitable choice because it fills the space between the line and the baseline with color, visually emphasizing the cumulative total over time. This allows the analyst to see both the trend of monthly sales and the stacked contribution of each month to the overall cumulative effect, which is exactly what the question requires.

Answer

Line chart

Answer

Stacked bar chart

Answer

Waterfall chart

Question 13

A data analyst is building a dashboard for executives and wants to ensure the most important metric, total revenue, is immediately visible. Which design principle should the analyst apply?

Accepted Answer

Visual hierarchy. Visual hierarchy means arranging elements so that the most important information is most prominent, often by placing it in the top-left or center and making it larger.

Answer

Appropriate precision

Answer

Consistent color coding

Answer

Data-ink ratio

Question 14

Which of the following is a key difference between a Key Performance Indicator (KPI) and a metric?

Accepted Answer

KPIs are tied to strategic objectives, while metrics are broader operational measurements. KPIs are tied to strategic objectives and measure progress toward goals, while metrics can be any operational measurement that may not be directly linked to strategy.

Answer

Metrics are always quantitative, while KPIs can be qualitative

Answer

There is no difference; the terms are interchangeable

Answer

KPIs are always lagging indicators, while metrics are leading indicators

Question 15

A business analyst wants to show the relationship between advertising spend (independent variable) and sales revenue (dependent variable) for 12 different regions. Each region will be represented by a point. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart type because it directly visualizes the relationship between two continuous variables—advertising spend (independent) and sales revenue (dependent)—by plotting each of the 12 regions as a point. This allows the analyst to assess correlation, trends, and outliers, which is the core purpose of scatter plots in data visualization.

Answer

Line chart

Answer

Bar chart

Answer

Histogram