MLA-C01 · Question #202
A company uses an Amazon QuickSight dashboard to track the sale prices of sneakers over time. The dashboard aggregates sale prices scraped from many retail websites. The company wants to determine…
The correct answer is C. Use a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to square the. Amazon QuickSight anomaly detection insights automatically identify unusual values in time- series data, such as abnormally high sneaker prices. These detected outliers can then be visualized directly in QuickSight charts, allowing the company to highlight unusually high prices…
Question
A company uses an Amazon QuickSight dashboard to track the sale prices of sneakers over time. The dashboard aggregates sale prices scraped from many retail websites. The company wants to determine which prices are unusually high outliers and to display the outliers visually. Which solution will meet these requirements?
Options
- AUse a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to take the
- BUse AWS Glue DataBrew to preprocess the data. Set the REMOVE_OUTLIERS operation to
- CUse a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to square the
- DUse a QuickSight filter to find the lowest 10 values for sneaker price. Assign a specific color to the
How the community answered
(53 responses)- A9% (5)
- B2% (1)
- C85% (45)
- D4% (2)
Explanation
Amazon QuickSight anomaly detection insights automatically identify unusual values in time- series data, such as abnormally high sneaker prices. These detected outliers can then be visualized directly in QuickSight charts, allowing the company to highlight unusually high prices without building custom detection logic.
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