Robert Bosch Hash Tables Quiz 1

Explanation:
A hash table is a data structure that uses a hash function to map keys to values. It allows for efficient insertion, deletion, and retrieval of elements based on their keys.

Explanation:
The time complexity of searching for an element in a hash table is typically O(1). This is because the hash function allows for direct access to the location where the element is stored.

Explanation:
Binary search is not a collision resolution technique used in hash tables. It is a searching algorithm that operates on sorted arrays or lists.

Explanation: The load factor of a hash table is the ratio of the number of elements stored to the total number of slots in the hash table. It determines the average number of elements stored in each slot and affects the performance of the hash table.

 

Explanation: Linear probing is a collision resolution technique in hash tables that can lead to clustering. Clustering occurs when consecutive elements are placed in adjacent slots, causing longer probe sequences and reduced performance.

Explanation: The worst-case time complexity of inserting an element into a hash table is O(n). This occurs when there are many collisions, resulting in the need to probe through multiple slots to find an empty one.

Explanation: An array is commonly used to implement a hash table. Each slot in the array represents a bucket where elements with the same hash value are stored.

Explanation: The space complexity of a hash table is typically O(n), where n is the number of elements stored in the hash table. This is because additional space is required to handle collisions and store the elements.

Explanation: While a good hash function should be deterministic, uniform distribution, and reversible (if necessary), it does not necessarily need to be collision-free. Collisions can occur in hash functions, and the hash table should have a mechanism to handle them.

Explanation: The primary advantage of using a hash table is efficient insertion and deletion of elements. Hash tables provide constant-time complexity for these operations on average, making them suitable for scenarios where frequent insertions and deletions are required.