Top 50 Data Structure Interview Questions & Answers (2026)
📘 Beginner
40 Questions
🎯 Fresher Friendly
🕒 Updated Mar 2026
A Data Structure is a way of organizing and storing data so that it can be accessed and worked with efficiently.
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There are two types: Linear (Arrays, Linked List, Stack, Queue) and Non-Linear (Tree, Graph).
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An Array is a collection of elements of the same type stored in contiguous memory locations.
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Linked List is a linear data structure in which elements (nodes) are connected by pointers.
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Singly Linked List, Doubly Linked List, and Circular Linked List.
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Stack is a linear data structure that follows LIFO (Last In, First Out) principle.
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Push, Pop, Peek, and isEmpty.
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Queue is a linear data structure that follows FIFO (First In, First Out) principle.
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Simple Queue, Circular Queue, Priority Queue, and Deque.
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A Circular Queue connects the last position back to the first position to make a circle.
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A Tree is a hierarchical data structure consisting of nodes connected by edges.
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A Binary Tree is a tree in which each node can have at most two children.
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A BST is a binary tree where the left child has smaller value and right child has greater value than the parent.
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A Graph is a non-linear data structure consisting of vertices and edges.
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Tree has a hierarchical structure with one root, Graph can have cycles and multiple connections.
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Recursion is a function calling itself directly or indirectly to solve a problem.
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A Hash Table stores key-value pairs and uses a hash function to compute an index into an array.
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Hashing is the process of converting a key into an address using a hash function.
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When two different keys generate the same hash value, it is called a collision.
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Chaining, Open Addressing (Linear, Quadratic Probing, Double Hashing).
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Stack follows LIFO, Queue follows FIFO.
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Array has fixed size and contiguous memory, Linked List has dynamic size and non-contiguous memory.
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Time complexity represents the amount of time an algorithm takes to complete as a function of input size.
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Space complexity represents the total memory used by an algorithm.
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Sorting algorithms arrange data in a specific order. Examples: Bubble, Selection, Insertion, Merge, Quick Sort.
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Bubble Sort repeatedly swaps adjacent elements if they are in the wrong order.
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Merge Sort is a divide and conquer algorithm that divides the array and merges sorted parts.
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Quick Sort selects a pivot and partitions the array into two halves, recursively sorting each half.
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Insertion Sort inserts each element into its correct position in a sorted part of the array.
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Selection Sort repeatedly selects the smallest element and places it at the correct position.
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Linear Search checks each element until the desired element is found.
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Binary Search repeatedly divides the sorted array into halves to find an element.
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Big O represents the worst-case time complexity of an algorithm.
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BFS explores level by level using Queue; DFS explores depth first using Stack or Recursion.
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A Heap is a complete binary tree that satisfies the heap property — max-heap or min-heap.
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A priority queue is a data structure where each element has a priority, and elements with higher priority are dequeued first.
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An adjacency matrix is a 2D array representation of a graph where cell[i][j] = 1 if there is an edge from i to j.
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An adjacency list stores a list of neighbors for each vertex in a graph.
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A balanced binary tree is a tree where the height of left and right subtrees differ by at most one.
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AVL Tree is a self-balancing binary search tree where the height difference of subtrees is at most one.
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