MOST IMPORTANT HEAP INTERVIEW QUESTIONS WITH THEIR SOLUTIONS

Sure, here are all the questions and answers:

Question 1: What is a heap data structure?
Answer: A heap is a complete binary tree that satisfies the heap property: each node’s value is greater than or equal to its children’s values.

Question 2: What are the two types of heaps?
Answer: Max-heap and min-heap. In a max-heap, the root node has the maximum value, while in a min-heap, the root node has the minimum value.

Question 3: What is the time complexity of inserting an element into a heap?
Answer: O(log n), where n is the number of elements in the heap.

Question 4: What is the time complexity of deleting an element from a heap?
Answer: O(log n), where n is the number of elements in the heap.

Question 5: What is the time complexity of finding the minimum or maximum element in a heap?
Answer: O(1), as the root node always contains the minimum or maximum element.

Question 6: What are the applications of heaps?
Answer: Heap applications:

1. Priority queues
2. Sorting
3. Finding the median
4. Implementing Dijkstra’s algorithm
5. Network routing
6. Huffman coding

Question 7: What is the difference between a heap and a binary search tree (BST)?
Answer: A heap is a complete binary tree that satisfies the heap property, while a BST is a partially ordered binary tree that satisfies the BST property.

Question 8: How do you convert a BST into a heap?
Answer: By performing an in-order traversal of the BST and inserting the elements into a heap.

Question 9: How do you merge two heaps?
Answer: By creating a new heap and inserting the elements from both heaps into the new heap while maintaining the heap property.

Question 10: What is the difference between a heap and a priority queue?
Answer: A heap is a data structure, while a priority queue is an abstract data type that can be implemented using a heap.

Question 11: What are the advantages of using a heap?
Answer: Advantages of using a heap:

1. Efficient insertion and extraction (O(log n))
2. Can be used to implement priority queues
3. Can be used for sorting (O(n log n))
4. Useful for other applications, such as finding the median and implementing Dijkstra’s algorithm