- A O(N)
- B O(N log N)
- C O(N^2)
- D O(1)
Time Complexity - Quiz
- A O(N)
- B O(log N)
- C O(N^2)
- D O(N log N)
- A O(N)
- B O(log N)
- C O(N^2)
- D O(V + E), where V is the number of vertices and E is the number of edges.
- A O(N)
- B O(log N)
- C O(2^N)
- D O(N^2)
- A O(1)
- B O(log N)
- C O(N)
- D O(N log N)
- A O(N)
- B O(log N)
- C O(N^2)
- D O(N log N)
- A O(1)
- B O(log N)
- C O(N)
- D O(N log N)
- A O(N)
- B O(log N)
- C O(N^2)
- D O(V + E), where V is the number of vertices and E is the number of edges.
- A O(N)
- B O(log N)
- C O(N^2)
- D O(Nk), where k is the number of digits in the maximum number.
- A O(N)
- B O(log N)
- C O(N^2)
- D O(N + k), where k is the range of input values.
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🟢 Correct Answers: 🔴 Wrong Answers:
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Time Complexity - Quiz
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Time Taken: Questions: Answered: Not Answered:
Correct Answer:
Wrong Answer:
Percentage: %
- A. O(N)
- B. O(N log N)
- C. O(N^2)
- D. O(1)
Remarks:
Explanation:
Bubble sort has a time complexity of O(N^2), making it inefficient for large datasets. It involves repeated swapping of adjacent elements until the entire list is sorted.
- A. O(N)
- B. O(log N)
- C. O(N^2)
- D. O(N log N)
Remarks:
Explanation:
Merge sort has a time complexity of O(N log N), making it a stable and efficient sorting algorithm.
- A. O(N)
- B. O(log N)
- C. O(N^2)
- D. O(V + E), where V is the number of vertices and E is the number of edges.
Remarks:
Explanation:
The breadth-first search (BFS) algorithm has a time complexity of O(V + E) in a graph.
- A. O(N)
- B. O(log N)
- C. O(2^N)
- D. O(N^2)
Remarks:
Explanation:
The recursive generation of the Fibonacci sequence has an exponential time complexity of O(2^N).
- A. O(1)
- B. O(log N)
- C. O(N)
- D. O(N log N)
Remarks:
Explanation:
In the worst-case scenario, a hash table may have a time complexity of O(N) for searching an element, depending on collisions and hash function behavior.
- A. O(N)
- B. O(log N)
- C. O(N^2)
- D. O(N log N)
Remarks:
Explanation:
Merge sort has a time complexity of O(N log N), making it a stable and efficient sorting algorithm.
- A. O(1)
- B. O(log N)
- C. O(N)
- D. O(N log N)
Remarks:
Explanation:
Searching an element in a balanced binary search tree has a time complexity of O(log N), where N is the number of nodes.
- A. O(N)
- B. O(log N)
- C. O(N^2)
- D. O(V + E), where V is the number of vertices and E is the number of edges.
Remarks:
Explanation:
The time complexity of BFS in a graph is O(V + E), where V is the number of vertices and E is the number of edges.
- A. O(N)
- B. O(log N)
- C. O(N^2)
- D. O(Nk), where k is the number of digits in the maximum number.
Remarks:
Explanation:
Radix sort has a time complexity of O(Nk), where N is the number of elements and k is the number of digits in the maximum number.
- A. O(N)
- B. O(log N)
- C. O(N^2)
- D. O(N + k), where k is the range of input values.
Remarks:
Explanation:
Counting sort has a time complexity of O(N + k), where N is the number of elements and k is the range of input values.