Visualizing Data

A data analyst is creating a dashboard to monitor server CPU utilization over the past 24 hours. Which chart type is most appropriate for showing the trend of CPU usage over time?

A sales dashboard shows monthly revenue but the bars are very tall for some months and very short for others, making comparisons difficult. Which visualization modification would best improve readability?

A data analyst creates a bubble chart showing country GDP (x-axis), life expectancy (y-axis), and population (bubble size). However, large bubbles overlap and obscure many data points. Which corrective action should the analyst take?

An analyst wants to show the distribution of test scores for 500 students. Which visualization type is best for understanding the shape of the distribution?

A dashboard shows sales by region using a map with color intensity. Users complain that two regions with very different sales appear nearly the same color. What is the most likely cause?

An analyst creates a stacked bar chart showing quarterly sales by product category. The chart becomes hard to read because some categories have very small contributions. Which redesign is most effective?

A data analyst needs to present the correlation between advertising spend and website traffic. Which chart type is most appropriate?

An analyst builds a dashboard with a gauge showing 'Current Inventory Level' as a percentage. Stakeholders find the gauge misleading because it always shows near 100% even when inventory is low. What is the most likely issue?

A data analyst creates a heatmap to show website click-through rates by hour and day of week. The heatmap uses a green-to-red gradient, but users cannot distinguish between moderate values. What is the best fix?

Which TWO actions are best practices for creating effective data visualizations?

Which THREE factors should be considered when choosing a chart type for a dataset?

Which TWO visualization types are suitable for showing the distribution of a single continuous variable?

You are a data analyst for an e-commerce company. Your team has built a dashboard to monitor daily sales performance across five regions: North, South, East, West, and Central. The dashboard includes a bar chart showing total sales per region, a line chart showing daily sales trend over the past 30 days, and a pie chart showing sales distribution by product category (Clothing, Electronics, Home, Books, Sports). Recently, stakeholders have complained that the pie chart is hard to interpret because the Sports category has very small sales and is barely visible. Also, the bar chart uses a rainbow color scheme that makes it difficult to compare bar heights because the colors are not ordered by magnitude. The line chart is fine. You need to redesign the dashboard to address these issues. Which combination of changes is most appropriate?

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?

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?

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?

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?

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

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

Match each data type to its example.

Match each data analysis tool to its primary function.

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

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?

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?

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

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?

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?

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?

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?

A dataset contains salaries ranging from $25,000 to $2,500,000, with most salaries under $100,000. Which chart type best shows the distribution without distortion from extreme values?

Which TWO are best practices for designing effective dashboards? (Select exactly two.)

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

Which TWO chart types are best suited to show the proportion of total sales contributed by each product category? (Select exactly two.)

A dashboard designer wants to highlight the sales performance of individual sales representatives compared to team averages. Which chart type is most suitable for this comparison?

An analyst creates a pie chart showing market share of four companies: A (45%), B (30%), C (15%), D (10%). A stakeholder complains that it is difficult to compare C and D. Which alternative chart should the analyst recommend?

A data analyst is tasked with visualizing the distribution of customer ages across different regions. The dataset contains outliers. Which chart type best displays the distribution and highlights outliers?

A dashboard uses a heatmap to show sales density by hour and day of week. Users report that the color scale is confusing because some low values appear similar to high values. Which design change improves clarity?

An analyst is creating a report that includes multiple charts. To ensure the audience quickly grasps the key insight, which principle of data storytelling should be applied?

A data visualization specialist needs to display the relationship between advertising spend and revenue for 50 product categories over 12 months. The data has many overlapping points. Which chart type best reveals the correlation and density?

A company uses a dashboard to monitor server uptime. The data is collected every minute, but the dashboard only refreshes every hour. Users see gaps in the line chart. What is the most likely cause, and how should it be fixed?

An analyst creates a histogram of customer transaction amounts but observes that the distribution looks bimodal. Upon review, the analyst realizes that two different customer segments (retail and wholesale) were combined. Which action best addresses this?

A data analyst wants to show the relative proportions of defects by type in a manufacturing process. There are 6 defect types. Which chart is most appropriate?

Which TWO of the following are best practices for designing an accessible data visualization? (Choose 2.)

Which TWO of the following chart types are appropriate for showing the distribution of a continuous variable? (Choose 2.)

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

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?

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?

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?

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?

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

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?

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

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

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

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

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

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

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

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

Which TWO color choices are appropriate for a categorical data visualization? (Select two.)

Refer to the exhibit. A data analyst attempts to visualize the dataset but receives a permission error. The analyst's username is 'analyst2'. What is the most likely cause?

Refer to the exhibit. What is the best corrective action to resolve this error?

Refer to the exhibit. A stakeholder complains that the line chart exaggerates the changes in sales. What is the most likely cause?

A company needs to visualize monthly sales revenue for the past five years to identify seasonal trends. Which chart type is most appropriate?

A data analyst is designing a dashboard for executives. Which best practice should be followed regarding the placement of key performance indicators (KPIs)?

A data analyst creates a bar chart to compare average customer satisfaction scores across five departments. The chart shows very tall bars for three departments and very short bars for two departments, making differences hard to assess. What is the most likely cause and the best fix?

A business analyst wants to compare the proportion of total sales contributed by each product category in the current year. Which visualization is most suitable?

An analyst creates a dashboard with multiple visualizations. Which feature allows users to change the data displayed across all charts simultaneously?

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?

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?

A dashboard designer needs to ensure that color choices are accessible to users with color vision deficiencies. Which practice should be followed?

Which TWO actions will improve the readability of a bar chart showing quarterly sales across five regions?

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

Which TWO are common mistakes when creating data visualizations?

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?

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?

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?

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?

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.)

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?

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?

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?

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?

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?