by fdach
Dive into the intriguing world of algorithm efficiency with our Big O Notation Quiz. This quiz will challenge your knowledge of how algorithms perform and scale, helping you to better understand their complexities and efficiencies. Whether you’re a seasoned developer or just starting out, this quiz will provide valuable insights into one of the core concepts of computer science. Ready to put your skills to the test? Let’s get started!
We recommend that you do not leave the page that you are taking this quiz in. Stay honest 🙂
Big O Notation Quiz Questions Overview
1. What does Big O Notation primarily describe?
The correctness of an algorithm
The efficiency of an algorithm
The readability of an algorithm
The security of an algorithm
2. Which Big O Notation represents the best possible time complexity?
O(n)
O(log n)
O(1)
O(n^2)
3. What does O(n^2) signify in Big O Notation?
The algorithm’s runtime grows linearly with input size
The algorithm’s runtime grows quadratically with input size
The algorithm’s runtime is constant
The algorithm’s runtime grows logarithmically with input size
4. Which of the following is an example of O(log n) complexity?
Binary search
Linear search
Bubble sort
Insertion sort
5. What is the time complexity of the merge sort algorithm?
O(n log n)
O(n^2)
O(n)
O(log n)
6. Which Big O Notation represents the worst-case time complexity of quicksort?
O(n log n)
O(n^2)
O(n)
O(log n)
7. What is the space complexity of an algorithm that uses a constant amount of extra space?
O(n)
O(log n)
O(1)
O(n^2)
8. Which of the following algorithms has a time complexity of O(n)?
Binary search
Linear search
Merge sort
Quick sort
9. What is the time complexity of finding an element in a balanced binary search tree (BST)?
O(n)
O(log n)
O(n^2)
O(1)
10. Which Big O Notation represents the best-case time complexity of quicksort?
O(n log n)
O(n^2)
O(n)
O(log n)
11. What does O(n!) signify in Big O Notation?
The algorithm’s runtime grows linearly with input size
The algorithm’s runtime grows quadratically with input size
The algorithm’s runtime grows exponentially with input size
The algorithm’s runtime grows factorially with input size
12. Which of the following is an example of O(n log n) complexity?
Bubble sort
Insertion sort
Merge sort
Linear search
13. What is the time complexity of the bubble sort algorithm in the worst case?
O(n log n)
O(n^2)
O(n)
O(log n)
14. Which Big O Notation represents the average-case time complexity of quicksort?
O(n log n)
O(n^2)
O(n)
O(log n)
15. What is the space complexity of merge sort?
O(n)
O(log n)
O(1)
O(n^2)
16. Which of the following has a time complexity of O(log n)?
Searching in a balanced BST
Linear search
Bubble sort
Insertion sort
17. What is the time complexity of the insertion sort algorithm in the best case?
O(n log n)
O(n^2)
O(n)
O(log n)
18. Which Big O Notation represents the worst-case time complexity of binary search?
O(n log n)
O(n^2)
O(n)
O(log n)
19. What does O(2^n) signify in Big O Notation?
The algorithm’s runtime grows linearly with input size
The algorithm’s runtime grows quadratically with input size
The algorithm’s runtime grows exponentially with input size
The algorithm’s runtime grows factorially with input size
20. Which of the following algorithms has a time complexity of O(n^2)?
Binary search
Linear search
Bubble sort
Merge sort
21. What is the time complexity of finding the maximum element in an unsorted array?
O(n log n)
O(n^2)
O(n)
O(log n)
We recommend that you do not leave the page that you are taking this quiz in. Stay honest 🙂
