DSA interview questions for freshers

You grow a sorted prefix and insert the next element by shifting larger values right.

Big-O describes how runtime or memory grows as input size n grows, ignoring constants and lower-order terms.

On a sorted array, each step halves the search range, so time is O(log n) and extra space is O(1) iterative or O(log n) recursive.

Multiply independent iteration counts: an n-loop around an n-loop is O(n^2).

Time complexity is how the number of operations grows

The base case is the input that returns immediately without another recursive call, such as n == 0 for factorial.

Recurse on n-1 first, then print n, so the prints happen on the way back

Recursion matches tree and graph DFS, divide-and-conquer, and backtracking because the call stack stores the path.

A power of two has exactly one bit set, so n > 0 and (n & (n - 1)) == 0.

Brian Kernighan's trick repeatedly does n &= n - 1, clearing the lowest set bit until n is zero

Access by index is O(1) because the address is base plus index times size.

Kadane’s algorithm keeps a running best ending here.

O(1) average with a good hash.

Substring is contiguous

Put one array in a hash set and scan the other for hits.

Two pointers from ends moving inward comparing characters.

O(n log n) grows faster by a log factor — sorting is the classic case.