Top questions with answers asked in MNC on Data Structure

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 Data Structure interview questions along with their answers:
  1. What is a data structure, and why is it important?
    • Answer: A data structure is a way of organizing and storing data in a computer so that it can be accessed and manipulated efficiently. It defines a set of operations that can be performed on the data, such as insertion, deletion, searching, and sorting. Data structures play a crucial role in computer science and programming because they determine how data is stored, accessed, and processed by algorithms. By choosing the appropriate data structure for a given problem, developers can optimize performance, reduce memory usage, and improve code readability and maintainability.
  2. What is the difference between an array and a linked list?
    • Answer:
      • Array: An array is a contiguous block of memory that stores elements of the same data type. Elements in an array are accessed using an index, and the size of the array is fixed at compile time. Array operations such as accessing, inserting, or deleting elements have constant time complexity O(1) if the index is known but may have linear time complexity O(n) if the index needs to be searched.
      • Linked List: A linked list is a linear data structure in which elements are stored in nodes, and each node contains a value and a pointer/reference to the next node in the sequence. Unlike arrays, linked lists do not require contiguous memory allocation, allowing for dynamic memory allocation and flexibility in size. Linked lists support efficient insertion and deletion operations (O(1)) but have slower access time (O(n)) since elements must be traversed sequentially from the beginning or end of the list.
  3. What is the difference between a stack and a queue?
    • Answer:
      • Stack: A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle, meaning the most recently added element is the first one to be removed. It supports two main operations: push (adds an element to the top of the stack) and pop (removes the top element from the stack). Additional operations such as peek (retrieve the top element without removing it) and isEmpty (check if the stack is empty) are also commonly supported.
      • Queue: A queue is a linear data structure that follows the First-In-First-Out (FIFO) principle, meaning the first element added to the queue is the first one to be removed. It supports two main operations: enqueue (adds an element to the back/rear of the queue) and dequeue (removes the front element from the queue). Additional operations such as peek (retrieve the front element without removing it) and isEmpty (check if the queue is empty) are also commonly supported.
  4. What is the difference between a binary tree and a binary search tree (BST)?
    • Answer:
      • Binary Tree: A binary tree is a hierarchical data structure in which each node has at most two children, referred to as the left child and the right child. The children of a node are ordered, meaning there is a specific relationship between each child and its parent. Binary trees can have various shapes and structures, and they are used in applications such as expression trees, binary heaps, and hierarchical representations.
      • Binary Search Tree (BST): A binary search tree is a specific type of binary tree in which the value of each node is greater than all values in its left subtree and less than all values in its right subtree. This property enables efficient searching, insertion, and deletion operations with time complexity O(log n) on average (assuming the tree is balanced). BSTs are commonly used in applications such as searching, indexing, and sorting.
  5. What is the difference between depth-first search (DFS) and breadth-first search (BFS)?
    • Answer:
      • Depth-First Search (DFS): DFS is a graph traversal algorithm that explores as far as possible along each branch before backtracking. It starts at a given vertex (or node) and explores as far as possible along each branch before backtracking. DFS uses a stack (either implicitly through recursion or explicitly) to keep track of visited nodes and their neighbors. DFS is well-suited for problems such as finding connected components, topological sorting, and solving maze-like puzzles.
      • Breadth-First Search (BFS): BFS is a graph traversal algorithm that explores all the neighbor vertices at the present depth prior to moving on to the vertices at the next depth level. It starts at a given vertex (or node) and explores all its neighbors before moving on to the next level of neighbors. BFS uses a queue to keep track of visited nodes and their neighbors. BFS is well-suited for problems such as finding the shortest path, finding the connected components in an undirected graph, and solving puzzles with multiple solutions.