Fundamentals of Computing Practice Test 2026 – Your All-In-One Guide to Exam Success!

Time complexity describes how the runtime of an algorithm grows as the input size increases. This focuses on how many basic operations the algorithm performs as the amount of data grows, not on memory usage. Space complexity, on the other hand, is about memory usage, including any extra storage the algorithm requires beyond the input. Big-O notation captures these growth rates and is used to express both time and space, not only memory usage. So, describing how runtime grows with input size is the accurate statement.

For example, if an algorithm scans every element once, its time complexity is linear, O(n); doubling the input roughly doubles the running time.