Which runtime usually scales better for large inputs?

$O(n \log n)$ grows more slowly than $O(n^2)$, $O(2^n)$, or $O(n!)$ for large $n$.

Correct answer: $O(n \log n)$

Why is Big-O notation useful?

It focuses on asymptotic growth rather than machine-specific details.

Correct answer: It helps compare how algorithms scale as input size grows.

Which strategy typically solves a problem by combining solutions to smaller subproblems?

Divide and conquer breaks a problem into smaller pieces and merges the results.

Correct answer: Divide and conquer

Name one situation where a greedy algorithm might be appropriate.

Greedy methods work only when the problem has the right structure.

Correct answer: When locally optimal choices lead to a globally optimal solution, such as interval scheduling.