AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 158

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?

Answer options

• A. Use a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to take the square roots of the outlier prices to generate the chart. Configure a custom AWS Lambda function to scan the data for anomalies.
• B. Use AWS Glue DataBrew to preprocess the data. Set the REMOVE_OUTLIERS operation to eliminate data rows that include unusually high prices. Invoke an AWS Lambda function to store the removed rows in Amazon DynamoDB.
• C. Use a vertical bar chart to visualize the outliers. Use a calculated field in QuickSight to square the outlier prices to generate the chart. Use QuickSight anomaly detection insights to determine which prices are unusually high.
• D. Use a QuickSight filter to find the lowest 10 values for sneaker price. Assign a specific color to the 10 lowest values.

Correct answer: C

Explanation

Option C is correct because it effectively uses QuickSight's built-in anomaly detection capabilities to identify and visualize unusually high prices. Option A incorrectly suggests using the square root of outlier prices, which does not help in visualizing high outliers. Option B focuses on removing data points rather than visualizing them, and Option D does not address identifying high outliers, but rather the lowest prices.