DA0-002 Visualization and Reporting Practice Test 2 — 15 Questions

Question 1

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.

Answer

Pie chart

Answer

Bar chart

Answer

Line chart

Question 2

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.

Answer

Scatter plot

Answer

Histogram

Answer

Line chart

Question 3

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).

Answer

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

Answer

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

Answer

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

Question 4

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.

Answer

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

Answer

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

Answer

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 5

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.

Answer

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

Answer

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

Answer

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

Question 6

Match each data type to its example.

Accepted Answer

Nominal: Colors of cars (red, blue, green). Nominal data are categories without order; ordinal data have ranked categories; interval data have equal intervals but no true zero; ratio data have equal intervals and a true zero. Common confusions include mistaking nominal for ordinal (e.g., gender) or interval for ratio (e.g., counts).

Answer

Ordinal: Gender (male, female)

Answer

Interval: Number of siblings (0,1,2,3)

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.

Answer

Excel: Predictive modeling and statistical analysis

Answer

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. (line chart) is correct because line charts are best for showing trends over time. Option A (bar chart) is more for comparing categories, Option B (scatter plot) shows relationships between variables, and Option D (pie chart) shows parts of a whole.

Answer

Bar chart

Answer

Scatter plot

Answer

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. Using the median instead of the mean reduces the influence of extreme values. The median is a robust measure of central tendency that is not skewed by a single large order, thus providing a more accurate representation of typical order values in that region. Option A (logarithmic scaling) could help with visualization of wide ranges but does not change the aggregation; option C (pie chart) is not suitable for comparing average values across regions; option D (removing all outliers) is too drastic and may discard valid data points.

Answer

Apply logarithmic scaling to the y-axis

Answer

Change the chart type to a pie chart

Answer

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. A choropleth map using rates (sales per capita) normalizes for population, preventing small-population states with high sales from appearing overly prominent. Options A, C, and D do not properly normalize the data: A changes chart type but does not normalize, C uses raw sales, and D applies a log scale which distorts the relationship without normalizing for population.

Answer

Switch to a bar chart sorted by sales

Answer

Use a bubble chart with size proportional to sales

Answer

Apply a log scale to the color gradient

Question 11

A marketing team wants to compare the growth rates of four product categories over the last quarter. Which chart type would best display this?

Accepted Answer

Bar chart. A bar chart is ideal for comparing discrete values across categories, such as the growth rates of four product categories. It allows for easy visual comparison of the heights of the bars. Option A (pie chart) is unsuitable for comparing growth rates as it shows proportions of a whole, not rates. Option B (stacked bar chart) would show cumulative totals, not individual growth rates. Option C (line chart) is better for trends over time, not for comparing discrete categories.

Answer

Pie chart

Answer

Stacked bar chart

Answer

Line chart

Question 12

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. Adding a trendline (e.g., linear regression) can reveal underlying patterns in noisy scatter plot data, making it easier to see correlations that are not immediately obvious from the raw points alone. Other options like changing to a bar chart, increasing marker size, or removing data points would not help reveal a hidden trend and may obscure the data.

Answer

Change to a bar chart

Answer

Increase the marker size

Answer

Remove data points with low spend

Question 13

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. PCA reduces the dimensionality of a 50-variable dataset to two principal components while preserving the variance, making clusters visible in a scatter plot. Parallel coordinates (C) is useful for visualizing high-dimensional data but does not inherently preserve cluster structure. Heatmap of correlations (A) shows relationships between variables, not observations. Scatter matrix (B) becomes impractical with 50 variables due to the number of plots, and it does not reduce dimensionality.

Answer

Heatmap of correlations

Answer

Scatter matrix (pairplot)

Answer

Parallel coordinates plot

Question 14

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 allows the audience to easily compare trends across the five regions over time. Each region can be represented by a separate line, making it simple to see upward or downward trends and compare them side by side. In contrast, a pie chart shows parts of a whole and is not suitable for trend comparison; a stacked bar chart emphasizes composition rather than trends; and an area chart is similar to a line chart but can obscure overlapping trends due to stacking.

Answer

Pie chart

Answer

Stacked bar chart

Answer

Area chart

Question 15

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. KPI cards (or tiles) are designed to display a single key metric, such as revenue, profit margin, or customer count, along with its variance (e.g., change over time or vs. target), making them ideal for executive dashboards where quick comprehension of high-level KPIs is critical. Pie charts are poor for showing individual values and cannot display variance; sparklines show trends but not the current value prominently; gauge charts are less precise for exact numbers. Therefore, KPI cards are the most appropriate choice.

Answer

Pie chart

Answer

Sparkline for each KPI

Answer

Gauge chart