Algorithms Analysis Practice Test 2025 – All-in-One Mastery Guide to Exam Success

The Big-Oh complexity associated with traversing a linked list is O(n). This is because, in a linked list, each element (or node) is connected to the next one through pointers, and there is no direct access to the elements as there would be in an array.

When traversing a linked list, you start at the head node and move to each subsequent node one at a time until you reach the end of the list. Since you have to visit each element to measure, print, or process the data stored, the time taken grows linearly with the number of elements in the list. If there are n elements, the time taken to traverse the list is proportional to n, leading to a complexity of O(n).

On the other hand, complexities like O(1), O(n^2), and O(2^n) do not accurately describe the traversal of a linked list. O(1) indicates a constant time operation, which is not applicable for traversing as it requires visiting multiple nodes. O(n^2) suggests a situation where nested traversals occur, which is not the case in a single linked list traversal. O(2^n) typically refers to exponential growth, often seen in recursive algorithms