DA0-002 Visualization and Reporting Practice Test 8 — 15 Questions

Question 1

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 is an interactive control that, when changed, applies the same selection criterion to every visualization on the dashboard, enabling simultaneous cross-chart updates. This is the standard mechanism in BI tools (Tableau, Power BI, Looker) for synchronized data selection.

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Linked chart

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Drill-down

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Data segmentation

Question 2

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. Outliers in a box plot represent data points that fall outside the typical range (beyond 1.5×IQR). They may be legitimate extreme values or errors. Investigating them is essential to understand whether they are valid (e.g., highly compensated executives) or mistakes (e.g., data entry errors). Removing or trimming them without investigation could distort the analysis and hide important insights.

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Replace the box plot with a histogram of the salaries

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Remove all outliers to create a more typical box plot

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

Question 3

A logistics company has data on delivery times (continuous) and distance traveled (continuous). They want to visualize the relationship between these two variables. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the correct choice because it displays individual data points on a two-dimensional plane, with one continuous variable on the x-axis (distance traveled) and the other on the y-axis (delivery time). This allows analysts to visually assess the strength, direction, and form of the relationship between two continuous variables, including spotting correlations, clusters, or outliers. Unlike other charts, scatter plots are specifically designed for bivariate continuous data, making them ideal for this logistics use case.

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Histogram

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Bar chart

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Line chart

Question 4

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 through multiple channels, not just color. This makes the visualization accessible to users with color vision deficiencies, who may not distinguish certain colors. It also benefits users in grayscale printing or low-quality displays. This practice aligns with WCAG guidelines for using color as a supplementary, not sole, means of conveying information.

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Use a rainbow color palette to maximize differentiation

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Convert all charts to grayscale

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Use red and green to indicate positive and negative values

Question 5

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. Option B is correct because sorting bars in descending order of sales arranges the categories by magnitude, letting viewers instantly rank the five regions and spot the highest and lowest performers without scanning back and forth. Option C is correct because data labels placed on top of each bar display the exact sales values directly, eliminating the need to estimate values against an axis and reducing reliance on gridlines. Together these two changes make the chart's message immediately clear. Option A does not belong because overlaying a cumulative line adds a second, differently scaled metric that complicates rather than clarifies the quarterly comparison. Option D does not belong because gridlines at every bar add visual clutter and compete with the bars instead of improving readability. Option E does not belong because 3D bar charts distort bar lengths through perspective, making values harder to compare accurately.

Answer

Overlay a line chart showing cumulative sales

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Add vertical gridlines for every bar

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Switch to a 3D bar chart to add visual depth

Question 6

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

Accepted Answer

Ensure the dashboard is responsive for mobile devices. Option B is correct because executives frequently consume dashboards on tablets and phones, so a responsive layout ensures KPIs remain legible and usable across screen sizes. Option D is correct because interactive filters for time periods and regions let executives drill into the specific slice of data relevant to their decision without needing a new report. Option E is correct because placing the most critical KPIs at the top follows the principle of prioritized information hierarchy, ensuring the most important metrics are seen first. Option A is not ideal because detailed raw data tables overwhelm executive viewers and obscure high-level trends. Option C is not ideal because fragmenting every metric into its own chart creates visual clutter and prevents at-a-glance comparison of related KPIs.

Answer

Include detailed raw data tables for transparency

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Use a separate chart for each metric to avoid clutter

Question 7

Which TWO are common mistakes when creating data visualizations?

Accepted Answer

Using excessive 3D effects that obscure data. Option A is correct because excessive 3D effects distort proportions and depth cues, making values harder to compare accurately and often obscuring data points behind other elements. Option E is correct because starting the y-axis at a non-zero value on a bar chart exaggerates differences between bars, misleading viewers about the true magnitude of change. Option B is not a common mistake since alphabetical sorting is a legitimate, neutral ordering choice when no natural or quantitative order exists. Option C is not a mistake because color-blind friendly palettes improve accessibility and readability. Option D is not a mistake because a legend is a standard, helpful element for identifying chart elements.

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Sorting categories alphabetically in a bar chart

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Choosing a color-blind friendly palette

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Including a legend to identify chart elements

Question 8

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 category best shows monthly sales trends over time and allows comparison across categories to identify growth drivers. Annotating outliers (grand opening spikes) preserves data integrity while explaining anomalies, which is critical since removing them would distort the trend analysis.

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

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

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

Question 9

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?

Accepted Answer

Use a heatmap showing CPU utilization over time per server, or create small multiple charts (one per server). With 200 servers and 100,000+ records per server, a single scatter plot is too cluttered. A heatmap can show CPU utilization over time per server using color intensity, or small multiples (one chart per server) can separate the data. Both approaches reduce overlap and make patterns like consistent over-80% utilization and spikes visible.

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

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

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

Question 10

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. A stacked bar chart with departments on the x-axis and counts of each rating stacked shows the full distribution of ordinal responses per department, making it easy to compare which department has the most 'Very Satisfied' (top stack) and 'Very Dissatisfied' (bottom stack) responses. It also allows visual comparison of the spread across departments by comparing stack heights and segment proportions.

Answer

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

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Box plot for each department

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Scatter plot with department as category and satisfaction score as value

Question 11

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 the most appropriate chart to visualize the relationship between two continuous variables, such as sales revenue and advertising spend. It plots each data point on a two-dimensional plane, allowing the analyst to observe patterns, trends, and potential correlation.

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Pie chart

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Line chart

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Bar chart

Question 12

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.. Option A is correct because a map visualization plots marks using geographic coordinates, so if latitude and longitude fields are missing, malformed, or not properly geocoded, the tool cannot place those regions and renders null or blank locations. Option B is correct because if the underlying data source lacks the region names (or uses names that don't match the geocoding dictionary), the map cannot resolve those regions to coordinates, again producing null values. Option C is wrong because filtering out nulls merely hides the affected regions rather than fixing the missing geographic data, so the visualization would still be incomplete. Option D is wrong because changing the mark type to Pie abandons the map entirely and does not address the geocoding or data completeness problem. Option E is wrong because removing the color encoding from profit only affects color, not the missing geographic positions that cause the null regions.

Answer

Add a filter to exclude null values.

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Change the mark type from Map to Pie.

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Remove the color encoding from profit.

Question 13

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.. The analyst should use a line chart for the overall monthly sales trend to show the aggregated pattern over two years, and a separate bar chart for the top 5 stores to compare their performance. Tooltips can explain outlier values (e.g., Black Friday spikes) without distorting the overall trend. This approach handles missing data by aggregating (which can smooth over gaps) and addresses outliers by providing context rather than removing them, which could bias the analysis.

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

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

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

Question 14

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 the most appropriate choice for comparing discrete categorical totals such as total sales across four product categories. Bar length directly encodes the magnitude, making comparisons easy and accurate. It handles a small number of categories cleanly without the perceptual issues of pie charts.

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Pie chart

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Scatter plot

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Line chart

Question 15

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.

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Including a detailed company logo on every chart

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Adding a background image to make the dashboard more visually appealing

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