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

Question 1

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?

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

Accepted Answer

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

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

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

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

Question 2

A data analyst needs to visualize the relationship between two continuous variables: advertising spend (in dollars) and monthly sales (in units). Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart for visualizing the relationship between two continuous variables, such as advertising spend (dollars) and monthly sales (units). It displays individual data points on an x-y axis, allowing the analyst to observe correlation, clustering, or outliers between the two numeric fields. This aligns with the DA0-001 objective of selecting visualizations based on data type and analytical goal.

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Pie chart

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Bar chart

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Line chart

Question 3

A data analyst is creating a dashboard for a retail company. The dashboard should provide insights into sales performance across multiple dimensions. Which TWO chart types are best suited for showing the contribution of each product category to total sales?

Accepted Answer

Pie chart. A pie chart is ideal for showing the contribution of each product category to total sales because it visually represents parts of a whole, making it easy to compare proportions at a glance. The stacked bar chart also effectively shows category contributions within a total, allowing for both absolute and relative comparisons across different time periods or segments. Both chart types directly address the need to visualize proportional breakdowns of a single aggregate metric.

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Scatter plot

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Histogram

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Line chart

Question 4

You are a data analyst for a logistics company. The company has a fleet of delivery trucks and tracks performance metrics including delivery time, fuel consumption, and distance traveled. Management wants a dashboard to monitor driver efficiency and identify underperforming drivers. You have access to a dataset with columns: DriverID, Date, RouteID, Distance (miles), FuelUsed (gallons), DeliveryTime (minutes). The dataset contains 10,000 records from the past year. You need to create a visualization that allows management to quickly compare the average fuel efficiency (miles per gallon) of drivers and also see how consistent each driver's efficiency is. Which of the following approaches is the best course of action?

Accepted Answer

Create a box plot grouped by DriverID showing the distribution of MPG for each driver.. A box plot grouped by DriverID is the best choice because it simultaneously shows the central tendency (median MPG) and the spread (interquartile range and outliers) of each driver's fuel efficiency. This allows management to quickly compare average efficiency across drivers while also assessing consistency—drivers with narrow boxes are more consistent, while those with wide boxes or many outliers are erratic. The other options either fail to show distribution (bar chart, line chart) or require manual interpretation of consistency (scatter plot).

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Create a line chart with Date on the x-axis and MPG on the y-axis, with separate lines for each driver.

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Create a scatter plot with Distance on the x-axis and FuelUsed on the y-axis, color-coded by DriverID.

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Create a bar chart showing the average MPG for each driver.

Question 5

Drag and drop the steps to clean a dataset with missing values in the correct order.

Accepted Answer

Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes. Cleaning starts with identification, then analysis of missingness, strategy selection, application, and documentation.

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Step 1: Analyze missingness pattern, Step 2: Identify missing values, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes

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Step 1: Identify missing values, Step 2: Select imputation/removal strategy, Step 3: Analyze missingness pattern, Step 4: Apply the strategy, Step 5: Document changes

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Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Apply the strategy, Step 4: Document changes, Step 5: Select imputation/removal strategy

Question 6

Drag and drop the steps to resolve data integration conflicts in the correct order.

Accepted Answer

Step 1: Identify conflicts, Step 2: Analyze conflicts, Step 3: Standardize data, Step 4: Transform data, Step 5: Merge data. Conflict resolution starts with identification, analysis, standardization, transformation, and merging.

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Step 1: Identify conflicts, Step 2: Standardize data, Step 3: Analyze conflicts, Step 4: Transform data, Step 5: Merge data

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Step 1: Analyze conflicts, Step 2: Identify conflicts, Step 3: Standardize data, Step 4: Transform data, Step 5: Merge data

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Step 1: Identify conflicts, Step 2: Analyze conflicts, Step 3: Transform data, Step 4: Standardize data, Step 5: Merge data

Question 7

Match each data analysis tool to its primary function.

Accepted Answer

Excel: Spreadsheet analysis and data manipulation. Excel is for spreadsheets, SQL for databases, Python for programming/ML, and Tableau for visualization. Common confusions include assigning predictive modeling to Excel or dashboard creation to SQL.

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Excel: Predictive modeling and statistical analysis

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SQL: Creating interactive dashboards

Question 8

A company needs to visualize the trend of monthly sales revenue over the past two years. Which chart type is most appropriate?

Accepted Answer

Line chart. A line chart is the most appropriate for visualizing the trend of monthly sales revenue over time because it plots data points connected by lines, clearly showing the direction and pattern of change across a continuous time series. The x-axis represents time (months), and the y-axis represents revenue, allowing for easy identification of upward or downward trends, seasonality, and fluctuations. Unlike other chart types, line charts are specifically designed to highlight trends and changes over intervals.

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Bar chart

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Scatter plot

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Pie chart

Question 9

A data analyst creates a dashboard showing average order value by region. The chart indicates that one region has an unusually high average. Investigation reveals that the region has very few orders, but one large purchase inflates the average. Which data transformation should the analyst apply to improve the visualization?

Accepted Answer

Use median instead of mean for aggregation. The mean is highly sensitive to extreme values, so a single large purchase in a region with few orders skews the average upward and misrepresents the typical order value. The median is a robust measure of central tendency that resists outlier influence, so switching the aggregation from mean to median gives a truer picture of typical order value per region.

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Apply logarithmic scaling to the y-axis

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Change the chart type to a pie chart

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Remove all outliers from the dataset

Question 10

A data analyst needs to visualize sales per capita across U.S. states. States with small populations but high sales (e.g., Delaware) appear too prominent on a choropleth map. Which technique best addresses this issue?

Accepted Answer

Use a choropleth map with rates instead of raw sales. Using a choropleth map with rates (e.g., sales per capita) instead of raw sales normalizes the data by population, preventing states with small populations from appearing overly prominent. This addresses the issue where Delaware, with high sales but low population, dominates the map. Rates allow for fair comparison across states regardless of population size.

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Switch to a bar chart sorted by sales

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Use a bubble chart with size proportional to sales

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Apply a log scale to the color gradient

Question 11

A scatter plot of advertising spend vs. revenue shows no clear correlation, but the analyst suspects a relationship exists. Which addition to the plot could help reveal a hidden trend?

Accepted Answer

Add a trendline. A trendline (regression line) added to a scatter plot can reveal a relationship that is not obvious from raw points alone, such as a nonlinear or weak correlation. It summarizes the overall direction and strength of the relationship, helping the analyst detect hidden trends. Changing chart type, marker size, or removing data points does not reveal underlying correlation.

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Change to a bar chart

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Increase the marker size

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Remove data points with low spend

Question 12

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?

Accepted Answer

Scatter plot of first two principal components. A scatter plot of the first two principal components applies PCA to project 50-dimensional data onto two axes that capture the most variance, preserving the global structure and cluster separation. It is the standard dimensionality-reduction visualization for cluster exploration because distances between points in PC space approximate distances in the original feature space. Other listed techniques either show pairwise relationships or all dimensions without reducing to 2D.

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Heatmap of correlations

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Scatter matrix (pairplot)

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Parallel coordinates plot

Question 13

A dashboard needs to show sales trends for each of five regions over the past year. The intended audience wants to compare trends easily. Which chart type is best?

Accepted Answer

Line chart with multiple lines. A line chart with multiple lines is the canonical choice for showing trends over time across several categories because the x-axis represents the continuous time dimension and each line tracks one region's trajectory. It allows the audience to compare slopes, inflection points, and relative performance at a glance. Pie, stacked bar, and area charts obscure per-series trends or make cross-series comparison harder.

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Pie chart

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Stacked bar chart

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Area chart

Question 14

An executive dashboard must display high-level KPIs such as current revenue, profit margin, and customer count. Which visualization type is most appropriate for each KPI?

Accepted Answer

KPI card showing value and variance. A KPI card showing value and variance is the most appropriate visualization for executive dashboards displaying high-level KPIs such as current revenue, profit margin, and customer count. KPI cards present a single, clear metric with its current value and comparison to a target or previous period (variance), enabling executives to quickly assess performance. This format avoids the clutter of charts and focuses attention on the key number.

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Pie chart

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Sparkline for each KPI

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Gauge chart

Question 15

Which THREE actions improve the accessibility of data visualizations for users with visual impairments? (Select exactly three.)

Accepted Answer

Provide text alternatives for charts (e.g., data tables).. Option A is correct because providing text alternatives such as data tables gives screen reader users and others who cannot perceive the chart a non-visual equivalent of the data, satisfying WCAG 1.1.1 Non-text Content. Option C is correct because clear, descriptive labels (titles, axis names, legends, and data point labels) let users understand what the visualization represents without relying on visual inference or color alone. Option D is correct because sufficient color contrast between text/graphical elements and their background meets WCAG 1.4.3 and 1.4.11, making charts legible for users with low vision or color vision deficiencies. Option B does not belong because using only color to convey information fails WCAG 1.4.1 Use of Color and excludes users who cannot distinguish those colors. Option E does not belong because animated transitions can trigger vestibular issues and distract users, and they do not improve accessibility of the underlying data.

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Use only color to convey information.

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Add animated transitions between views.