Big O, Big Ω, Big Θ notations MCQsBy: Prof. Dr. Fazal Rehman | Last updated: May 14, 2025 20 Score: 0 Attempted: 0/20 Subscribe 1. What does Big O notation represent? (A) The lower bound of an algorithm’s running time (B) The upper bound of an algorithm’s running time (C) The average-case running time (D) The exact running time 2. What does Big Ω notation represent? (A) The lower bound of an algorithm’s running time (B) The upper bound of an algorithm’s running time (C) The average-case running time (D) The exact running time 3. What does Big Θ notation represent? (A) The lower bound of an algorithm’s running time (B) The upper bound of an algorithm’s running time (C) The average-case running time (D) Both the upper and lower bounds of an algorithm’s running time 4. Which of the following is the correct notation for the time complexity of binary search? (A) O(n) (B) Ω(n) (C) Θ(log n) (D) O(log n) 5. Which notation would you use to describe the best-case time complexity? (A) Big O (B) Big Ω (C) Big Θ (D) Small o 6. For an algorithm with time complexity T(n) = 3n² + 2n + 1, what is the Big O notation? (A) O(n) (B) O(n²) (C) O(n³) (D) O(log n) 7. For an algorithm with time complexity T(n) = 5n + 10, what is the Big Θ notation? (A) Θ(n) (B) Θ(n²) (C) Θ(log n) (D) Θ(1) 8. Which notation is used to describe the average-case complexity of an algorithm? (A) Big O (B) Big Ω (C) Big Θ (D) Any of the above, depending on the context 9. If an algorithm has a running time of O(2ⁿ), what type of complexity does it have? (A) Linear (B) Logarithmic (C) Polynomial (D) Exponential 10. Which of the following is true about Big O notation? (A) It gives the exact running time of an algorithm (B) It provides a lower bound on the running time (C) It provides an upper bound on the running time (D) It represents the average case of an algorithm 11. What is the Big Θ notation for the worst-case time complexity of quicksort? (A) Θ(n) (B) Θ(n log n) (C) Θ(n²) (D) Θ(log n) 12. For an algorithm with time complexity T(n) = 2ⁿ, what is the Big Ω notation? (A) Ω(1) (B) Ω(log n) (C) Ω(n) (D) Ω(2ⁿ) 13. If an algorithm has a running time described by the Big Θ notation Θ(n²), which of the following is true? (A) Its best-case running time is Ω(n²) (B) Its worst-case running time is O(n²) (C) Its average-case running time is Θ(n²) (D) All of the above 14. Which notation would you use to describe the growth rate of an algorithm that always performs a constant number of operations regardless of input size? (A) O(n) (B) Ω(n) (C) Θ(1) (D) O(log n) 15. For an algorithm with time complexity T(n) = n³ + n² + n, what is the Big O notation? (A) O(n) (B) O(n²) (C) O(n³) (D) O(n log n) 16. Which of the following correctly describes an algorithm with a time complexity of O(n!)? (A) Polynomial time (B) Exponential time (C) Factorial time (D) Logarithmic time 17. If the best-case and worst-case time complexities of an algorithm are both O(n²), what can be said about its average-case time complexity? (A) It is also O(n²) (B) It is O(n) (C) It is O(n log n) (D) It cannot be determined from the given information 18. Which of the following statements is true about Big O and Big Ω notations? (A) Big O gives the lower bound and Big Ω gives the upper bound (B) Big O gives the upper bound and Big Ω gives the lower bound (C) Both give the average-case complexity (D) Both give the exact running time 19. What is the Big Θ notation for an algorithm with a running time of 3n log n + 2n? (A) Θ(n) (B) Θ(n log n) (C) Θ(log n) (D) Θ(n²) 20. Which of the following best describes the relationship between Big O, Big Ω, and Big Θ notations? (A) Big Θ provides both the upper and lower bounds, while Big O and Big Ω provide only the upper and lower bounds, respectively (B) Big O provides both the upper and lower bounds, while Big Ω and Big Θ provide only the upper and lower bounds, respectively (C) Big Ω provides both the upper and lower bounds, while Big O and Big Θ provide only the upper and lower bounds, respectively (D) All three notations provide the exact running time of an algorithm Data Structures MCQs Basic Concepts Introduction to Data Structures Abstract Data Types (ADT) MCQs Complexity Analysis MCQs Time complexity MCQs Space complexity MCQs Big O, Big Ω, Big Θ notations MCQs Linear Data Structures MCQs Arrays MCQs One-dimensional arrays MCQs Multi-dimensional arrays MCQs Operations: traversal, insertion, deletion MCQs Linked Lists MCQs Singly linked list MCQs Doubly linked list MCQs Circular linked list MCQs Stacks MCQs Stack operations (push, pop, peek) MCQs Applications of stacks (expression evaluation, recursion) MCQs Queues MCQs Queue operations (enqueue, dequeue, front, rear) MCQs Types: Simple queue, circular queue, priority queue, deque MCQs Non-Linear Data Structures MCQs Trees MCQs Binary trees MCQs Binary Search Trees (BST) MCQs AVL Trees MCQs B-trees and B+ trees MCQs Tree traversal methods (in-order, pre-order, post-order) MCQs Heaps MCQs Min-heap MCQs Max-heap MCQs Heap operations (insertion, deletion, heapify) MCQs Applications of heaps (priority queues, heap sort) MCQs Graphs MCQs Graph representation (adjacency matrix, adjacency list) MCQs Graph traversal algorithms (DFS, BFS) MCQs Shortest path algorithms (Dijkstra’s, Bellman-Ford) MCQs Minimum Spanning Tree (Kruskal’s, Prim’s) MCQs Hashing MCQs MCQs Hash Tables Hash functions MCQs Collision resolution techniques (chaining, open addressing) MCQs Applications of hashing MCQs Sorting and Searching Algorithms MCQs Sorting Algorithms MCQs Bubble sort MCQs Selection sort MCQs Insertion sort MCQs Merge sort MCQs Quick sort MCQs Heap sort MCQs Searching Algorithms MCQs Linear search MCQs Binary search MCQs Interpolation search MCQs Miscellaneous Memory Management in data structures MCQs Dynamic memory allocation MCQs Garbage collection MCQs String Manipulation Algorithms MCQs Pattern matching (KMP, Rabin-Karp) MCQs String hashing MCQs Data Structures MCQs 1 Data Structures MCQs 2 Data Structures MCQs 3 Data Structures MCQs 4 Data Structures MCQs 5 Stacks Solved MCQs Queues MCQs pointer mcqs Array MCQs Related Posts:Big Data Technologies and NoSQL Databases MCQsBig Data MCQsHadoop MCQs - Big Data ScienceBig data analysis MCQsEssay on life in a big city- [500 words]networking MCQs, storage solutions, cloud computing MCQs, data center technologies MCQs.