- ASelection sort
- BQuick sort
- CInsertion sort
- DHeap sort
Data Structure - Quiz
- A O(1)
- B O(log n)
- C O(n)
- D O(n^2)
- 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(1)
- B O(log N)
- C O(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 + k), where k is the range of input values.
- A 1 2
- B 1 2 3
- C 0 1 2
- D Compilation error
- A5 10
- B10 15
- C5 10 15
- DRuntime error
- A30
- B 0
- CArrayIndexOutOfBoundsException
- DCompilation error
- A10 20 30
- B10 20
- CRuntime error
- DCompilation error
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🟢 Correct Answers: 🔴 Wrong Answers:
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Data Structure - Quiz
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Time Taken: Questions: Answered: Not Answered:
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Wrong Answer:
Percentage: %
- A. Selection sort
- B. Quick sort
- C. Insertion sort
- D. Heap sort
Remarks:
Explanation:
Insertion sort is a stable sorting algorithm, meaning that it preserves the relative order of equal elements in the sorted output. When two elements have equal keys, their original order is maintained after sorting. In contrast, selection sort, quick sort, and heap sort do not guarantee this property.
- A. O(1)
- B. O(log n)
- C. O(n)
- D. O(n^2)
Remarks:
Explanation:
Bubble sort is a simple comparison-based sorting algorithm that repeatedly steps through the array, compares adjacent elements, and swaps them if they are in the wrong order. In the worst case, where the array is sorted in reverse order, each pass of the algorithm will result in the largest unsorted element "bubbling" to the end of the array. This requires n-1 comparisons in the first pass, n-2 in the second pass, and so on, resulting in a quadratic time complexity of O(n^2).
- 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(1)
- B. O(log N)
- C. O(N)
- D. O(N^2)
Remarks:
Explanation:
The pop operation in a queue has a constant-time complexity of O(1), making it efficient for removing elements from the front of the queue.
- 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(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.
- A. 1 2
- B. 1 2 3
- C. 0 1 2
- D. Compilation error
Remarks:
Explanation: 1 2 3
- A. 5 10
- B. 10 15
- C. 5 10 15
- D. Runtime error
Remarks:
Explanation: Answer: c) 5 10 15
- A. 30
- B. 0
- C. ArrayIndexOutOfBoundsException
- D. Compilation error
Remarks:
Explanation: Answer: c) ArrayIndexOutOfBoundsException
- A. 10 20 30
- B. 10 20
- C. Runtime error
- D. Compilation error
Remarks:
Explanation: Answer: c) Runtime error
(ArrayIndexOutOfBoundsException)