A company tracks sneaker sale prices over time in an Amazon QuickSight dashboard using prices scraped from many retailers. The company wants to detect unusually high price outliers and show them visually. Which approach satisfies these requirements?

The correct answer leverages QuickSight's built-in capabilities for anomaly detection and visualization. QuickSight anomaly detection insights are designed to identify unusual data points, such as unusually high prices, directly within your dashboard. A vertical bar chart is suitable for visualizing these identified outliers. Adding a calculated field that squares the price values can sometimes help emphasize differences or spread in data for visualization, though the primary mechanism for outlier detection here is QuickSight's anomaly detection. The first incorrect option suggests using a Lambda function for anomaly detection, which is unnecessary as QuickSight offers this natively. Taking the square root of prices is not a standard method for highlighting outliers. The second incorrect option removes outliers, which contradicts the requirement to detect and show them. Storing removed rows in DynamoDB doesn't help visualize them in QuickSight. The fourth incorrect option focuses on the 10 lowest prices, while the requirement is to detect unusually high outliers.