Commonly Asked Data Structure Interview Questions on Hash Data Structure

Question 1: What is a hash data structure?

Answer: A hash data structure is a data structure that stores key-value pairs, where the keys are hashed to determine the location of the value in the data structure.

Question 2: What is a hash table?

Answer: A hash table is a data structure that implements an associative array, allowing fast retrieval of values based on unique keys. It uses a hash function to map keys to indices in an array, providing constant-time average access (O(1)) if collisions are minimized.

Question 3: What is a hash function?

Answer: A hash function is a function that takes an input of any size and produces an output of a fixed size. The output is called a hash value or hash code.

Question 4: Explain how a hash function works.

Answer: A hash function takes an input key and maps it to a fixed-size index (hash value) within the hash table’s array. Ideally, the function distributes keys evenly across the array to minimize collisions. Common hash functions include modulo division, bitwise operations, and polynomial hashing.

Question 5: What is a collision?

Answer: A collision occurs when two different keys hash to the same value.

Question 6: Describe different collision resolution techniques.

Answer:

Open addressing: Use probing techniques (linear, quadratic, double hashing) to find the next available slot when a collision occurs.
Separate chaining: Store key-value pairs in linked lists at each index, leading to better performance for larger data sets.

Question 7: How are collisions handled in a hash data structure?

Answer: Collisions can be handled using various techniques, such as chaining, open addressing, and cuckoo hashing.

Question 8: What is chaining?

Answer: Chaining is a collision resolution technique where colliding keys are stored in a linked list at the same hash value.

Question 9: What is open addressing?

Answer: Open addressing is a collision resolution technique where colliding keys are stored in the same hash table, but at different locations.

Question 10: What is separate chaining?

Answer: Separate chaining is a collision resolution technique used in hash tables. When two or more keys hash to the same index in the hash table, instead of overwriting the existing data, separate chaining stores each key-value pair in a linked list at that index. This allows for efficient retrieval of all elements that hash to the same index, even if there are collisions.

Question 11: What are the trade-offs between open addressing and separate chaining?

Answer:

Open addressing: Less memory overhead, but search performance degrades with collisions.
Separate chaining: More memory usage, but faster search performance even with collisions.

Question 12: What is cuckoo hashing?

Answer: Cuckoo hashing is a collision resolution technique that uses two hash functions to store keys in a hash table.

Question 13: What is the load factor of a hash table?

Answer: The load factor of a hash table is the ratio of the number of keys stored in the table to the size of the table.

Question 14: What is the optimal load factor for a hash table?

Answer: The optimal load factor for a hash table depends on the collision resolution technique used, but it is typically around 0.7.

Question 15: Explain the concept of load factor and its impact on performance.

Answer: Load factor (number of elements / size of hash table) measures how full the table is. Higher load factors increase collision frequency and impact performance. Optimal values vary based on implementation and trade-offs.

Question 16: What are the advantages of using a hash data structure?

Answer: Hash data structures offer fast lookup, insertion, and deletion operations. They are also space-efficient and can handle large datasets.

Question 17: What are the disadvantages of using a hash data structure?

Answer: Hash data structures can suffer from collisions, which can slow down lookup operations. They also require a hash function that is both efficient and effective.

Question 18: Explain bloom filters and their applications.

Answer: Bloom filters use multiple hash functions to probabilistically represent set membership, offering efficient space-time trade-offs for membership queries but not supporting direct value retrieval. Used in caching, network security, and other applications.