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Cumulative frequency helps map each number to a range of positions in the sorted dataset. By comparing these ranges with the middle index (or indices), you can determine which value or values form the median.
Yes, median-related problems and SQL aggregation questions are common in data-focused interviews at companies like Amazon, Google, and Meta. Variants that involve frequency tables or window functions are especially relevant for data engineer and analyst roles.
The optimal approach uses cumulative frequency with SQL window functions. By calculating running totals ordered by the number column, you can identify the position where the median lies without expanding all values.
Window functions such as SUM() OVER(ORDER BY ...) are the most effective SQL feature for this problem. They allow you to compute cumulative frequencies that help determine the exact median position efficiently.