Path compression is a technique used to optimize the union operation in Disjoint Set Union, making future find operations faster.

The 'Heapify' algorithm is used to convert an array into a heap structure, ensuring the heap property is maintained.

Dijkstra's Algorithm is commonly used for finding the shortest path in a weighted graph.

Prim's algorithm uses a priority queue to efficiently select the next edge with the minimum weight to add to the spanning tree.

Dijkstra's Algorithm uses a priority queue to efficiently find the shortest path from a source vertex to all other vertices in a graph.

Hash tables allow for quick retrieval of phone numbers using names as keys, providing efficient access.

Hash tables are ideal for storing user sessions due to their fast access times and ability to handle dynamic data.

Disjoint Set Union can be efficiently used to manage connected components in a graph.

A priority queue is commonly implemented using a heap, as it allows for efficient insertion and deletion of the highest (or lowest) priority element.

Heaps are commonly used to implement priority queues because they allow for efficient insertion and removal of the highest (or lowest) priority element.

A queue is used for implementing breadth-first search (BFS) to explore nodes level by level.

Heap Sort is an algorithm that uses a binary heap to sort an array efficiently.

Dijkstra's Algorithm uses a priority queue to efficiently find the shortest path from a source node to all other nodes in a graph.

Dijkstra's Algorithm uses a priority queue to efficiently select the next vertex with the smallest tentative distance.

Kruskal's algorithm utilizes Disjoint Set Union to efficiently manage and merge sets of vertices while finding the minimum spanning tree.

Hash tables are commonly used to store user sessions in web applications for quick access and management of session data.

Cycle detection in a graph can benefit from using Disjoint Set Union, as it helps in efficiently managing and merging sets of vertices.

A good hash function should distribute keys uniformly across the hash table, and using a simple modulus operation is a common practice.

A good hash function distributes keys uniformly across the hash table to minimize collisions and ensure efficient access.

A good hash function often uses a prime number to help distribute keys uniformly across the hash table.

Cryptographic hash functions are designed to minimize collisions and provide a uniform distribution of hash values.

All listed techniques (chaining, linear probing, and quadratic probing) are methods used to handle collisions in hash tables.

Hash tables provide average-case O(1) time complexity for membership testing, making them efficient for set operations.

A binary heap is a complete binary tree, meaning all levels are fully filled except possibly for the last level.

Chaining is a common collision resolution technique where each slot in the hash table points to a linked list of entries that hash to the same index.

Chaining is a common collision resolution technique where each slot in the hash table points to a linked list of entries that hash to the same index.

Modular arithmetic is commonly used in hash functions to ensure that the hash value fits within the bounds of the array size.

One disadvantage of hash tables is the complexity involved in handling collisions, which can affect performance and implementation.

Hash tables do not maintain the order of elements, which is a characteristic of data structures like linked lists or trees.

A binary heap does not maintain a sorted order of elements; it only ensures the heap property (parent nodes are greater or less than child nodes).