DA0-002 Visualization and Reporting Practice Test 4 — 15 Questions

Question 1

Which THREE of the following are common mistakes when creating data visualizations? (Choose 3.)

Accepted Answer

Using a 3D pie chart. 3D pie charts distort the perception of proportions by adding a false depth dimension, making it difficult for viewers to accurately compare slice sizes. This violates the principle of data-ink ratio and is widely discouraged in data visualization best practices.

Answer

Choosing the correct chart type for the data

Answer

Labeling axes clearly

Question 2

The exhibit shows a SQL query result intended for a bar chart of revenue by region. However, the chart shows only the top 10 regions, but the query returns all regions. What is the most likely cause?

Accepted Answer

The visualization tool has a default limit on the number of categories displayed. The SQL query itself returns all regions because there is no LIMIT clause. However, the visualization tool has a built-in default limit on the number of categories displayed, such as top 10, which truncates the data in the chart. This is the most likely cause, making Option B correct. Options A, C, and D are incorrect: the GROUP BY clause is correctly specified, a WHERE clause is not required to get all regions, and the ORDER BY clause may be applied for sorting but the tool's limit overrides the full result set.

Answer

The GROUP BY clause is incorrect

Answer

The query is missing a WHERE clause

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The ORDER BY clause is ignored in the chart

Question 3

The exhibit shows a JSON configuration for a line chart that displays average response time over time. However, the chart shows jagged lines with many spikes. Which configuration change would smooth the visualization?

Accepted Answer

Change "interpolation" to "monotone". Setting interpolation to 'monotone' or 'smooth' can reduce jaggedness. The aggregation is already applied in the query, so changing it to 'average' in the widget would double-aggregate. Changing chart type or data source is not necessary.

Answer

Change "aggregation" to "average"

Answer

Change the query to include a moving average

Answer

Change chartType to "bar"

Question 4

The exhibit shows log entries. A data analyst wants to visualize the frequency of each error type over time. Which chart type is most appropriate?

Accepted Answer

Time series line chart. A time series line chart counts events per time interval, best for showing frequency over time. Bar chart of counts is static. Pie of error types ignores time. Scatter plot not suitable.

Answer

Bar chart of error types

Answer

Scatter plot of timestamp vs error code

Answer

Pie chart of error types

Question 5

A company wants to show the number of products sold across different categories: Electronics, Clothing, Home Goods, and Books. Which chart type is most appropriate?

Accepted Answer

Bar chart. Bar chart is ideal for comparing categorical data across distinct groups.

Answer

Scatter plot

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Line chart

Answer

Pie chart

Question 6

A data analyst is designing a dashboard for executives. Which best practice should be followed?

Accepted Answer

Minimize clutter and use clear visual hierarchy. Reducing clutter helps viewers focus on key insights without distraction.

Answer

Use 3D effects to make charts more engaging

Answer

Include every data point in the dashboard

Answer

Use rainbow color palette to highlight all data points

Question 7

A heat map of store sales by region shows very low correlation between advertising spend and revenue, but a scatter plot of the same data shows a strong positive relationship. What is the most likely cause?

Accepted Answer

Data was aggregated incorrectly in the heat map. Heat maps often aggregate data into bins (e.g., averages), which can mask the underlying relationship.

Answer

The heat map used an incorrect color scale

Answer

Outliers were removed only for the scatter plot

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The chart types are inherently incompatible

Question 8

A retail analyst wants to visualize monthly sales over the past year to identify seasonal patterns. Which chart type is best?

Accepted Answer

Line chart. A line chart is best for visualizing monthly sales over time because it explicitly shows trends and seasonal patterns across a continuous time axis. The sequential ordering of months allows the line to connect data points, making it easy to identify peaks, troughs, and recurring cycles. This aligns with the DA0-001 objective of selecting appropriate visualizations for time-series data.

Answer

Pie chart

Answer

Histogram

Answer

Box plot

Question 9

A data analyst needs to compare the salary distribution across five departments. Which visualization is most appropriate?

Accepted Answer

Side-by-side box plot. A side-by-side box plot (option B) is the most appropriate visualization for comparing salary distributions across multiple departments because it displays the median, quartiles, and potential outliers for each group simultaneously. This allows the analyst to assess central tendency, spread, and skewness across all five departments in a single, compact chart.

Answer

Line chart

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Scatter plot

Answer

Stacked bar chart

Question 10

A sales dashboard shows a map with many overlapping markers in the same city, making it hard to read. What is the best improvement?

Accepted Answer

Aggregate the data by region and use a choropleth map. Aggregating sales data by region and using a choropleth map eliminates visual clutter from overlapping markers by shading entire geographic areas based on a metric (e.g., total sales). This approach leverages spatial aggregation to provide a clear, high-level view of regional performance, which is the best practice when individual point markers become unreadable due to density.

Answer

Add tooltips to show details on hover

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Use a bubble chart instead of a map

Answer

Use different marker colors for each store

Question 11

A marketing team wants to explore the relationship between advertising spend (in dollars) and resulting revenue. Which chart type is most suitable?

Accepted Answer

Scatter plot. Scatter plots reveal correlation and distribution of two continuous variables.

Answer

Line chart

Answer

Table

Answer

Pie chart

Question 12

An executive dashboard needs to display key performance indicators (KPIs) such as sales growth and customer satisfaction. Which design principle is most important?

Accepted Answer

Consistent color encoding for similar metrics. Consistent color coding helps viewers quickly associate colors with metrics across multiple charts.

Answer

Use gauges for each KPI

Answer

Include all raw data in the dashboard

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Animate charts to draw attention

Question 13

A time series dataset has several missing months of data. Which chart type will present the most honest picture of the trend?

Accepted Answer

Line chart with gaps for missing months. Line charts with gaps explicitly show missing periods, avoiding false continuity.

Answer

Area chart with interpolation

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Bar chart with zero values for missing months

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Scatter plot with connected lines

Question 14

Which TWO chart types are best suited for visualizing the distribution of a single continuous variable? (Select two.)

Accepted Answer

Box plot. Histogram shows frequency bins; box plot shows quartiles and outliers.

Answer

Scatter plot

Answer

Line chart

Answer

Pie chart

Question 15

Which THREE are considered best practices in dashboard design? (Select three.)

Accepted Answer

Using heat maps to visualize correlation. Heat maps effectively visualize correlation by using color intensity to represent the strength of relationships between two variables, making patterns and outliers immediately apparent. This aligns with best practices for dashboard design, which prioritize clarity and rapid insight over decorative elements.

Answer

Using 3D charts to add depth

Answer

Including every data point in the dashboard