Data Structure Interview Questions and Answers in 2025

Last updated on Nov 26,2024 1.2K Views
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Data Structure Interview Questions and Answers in 2025

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Data structures are a way of organizing and storing data in a computer’s memory to facilitate efficient processing and retrieval of information. They provide a way to organize and structure data to perform operations on the stored information effectively. Many algorithms are based on the efficient use of data structures. Data structure-related questions are an integral part of technical interviews because they assess a candidate’s problem-solving skills, critical thinking, coding proficiency, and understanding of core computer science concepts. Being able to navigate data structure interview questions effectively can significantly contribute to success in technical interviews.

‘Bad programmers worry about the code. Good programmers worry about data structures and their relationships.‘ — Linus Torvalds.

Now, let’s explore the Data Structure Interview Questions in three sections- 

Data Structure Interview Questions for Freshers

1. What is a Data Structure? What are the characteristics of data structures?

A data structure is a way of organizing, storing, and managing data to facilitate efficient data access and modification. It defines a set of operations that can be applied to the data, as well as the relationships between the elements. The choice of a particular data structure depends on the nature of the data and the operations that need to be performed.

Characteristics of data structures

2. What are the two types of data structures? 

The two important types of data structures are linear and non-linear data structures.

  1. Linear Data Structures: Except for the first and last elements, linear data structures arrange elements in a sequential order, with each element having a distinct predecessor and successor. The elements form a linear sequence. Examples of linear data structures include linked lists, stacks, arrays, and queues.
  2. Non-linear Data Structures: In non-linear data structures, elements are not arranged in a sequential or linear order. Instead, elements can have relationships with multiple predecessors and successors, forming more complex structures. Common examples of non-linear data structures include: Trees, Graphs, Heaps, and Hash Tables.

3. Explain some common Data Structures.

4. Explain how an array differs from a linked list.

5. Define a linked list and its advantages over arrays.

A linked list is a linear data structure and consists of nodes. Each node points to the next node in a sequence, resulting in a linear chain.

Advantages of Linked Lists Over Arrays:

6. Describe the basic operations of a stack and a queue.

  1. Stack : A stack is a data structure that follows the Last In, First Out (LIFO) principle. The basic operations of a stack include:
  1. Queue : A queue is another data structure that follows the First In, First Out (FIFO) approach. The basic operations of a queue include:

7. What is a Queue, and how is it different from a Stack?

A queue is a data structure that follows the First In, First Out (FIFO) principle. In a queue, elements are added at the rear (enqueue) and removed from the front (dequeue). Queues are often used in scenarios where the order of processing is important, such as in task scheduling, printing, or managing requests in a network.

On the other hand, a stack is a data structure that follows the Last In, Last Out (LIFO) approach. In a stack, elements are added and removed from the same end, typically called the “top.” The last element pushed onto the Stack is the first to be popped off. Stacks are commonly used in situations where the order of processing is based on the order of arrival, such as in function calls, expression evaluation, or undo mechanisms.

8. What is a stack data structure? What are the applications of Stack?

A stack is a data structure that follows the Last In, First Out (LIFO) principle, i.e., the last element added to the Stack is the first one to be removed. 

Applications

9. What is a binary tree data structure? Explain the properties of a binary tree.

A binary tree is a hierarchical tree data structure in which each node can have no more than two children, known as the left and right children. Edges connect nodes in a binary tree, and there is a unique starting node called the root. The node without any children is called a leaf. Operations such as searching, insertion, and deletion can be performed efficiently in binary trees, especially when the tree is balanced. 

Here are the key properties of a binary tree:

10. Define a graph and explain the differences between directed and undirected graphs.

A graph is a computational representation of a set of objects, known as vertices or nodes, connected by edges. Graphs are used to model relationships between entities. The two main components of a graph are:

11. What are the differences between Directed and Undirected Graphs?

Now, let’s go through some common Data Structure interview questions for Experienced Professionals.

Data Structure Interview Questions for Experienced

1. What is a Deque?

A deque, or “double-ended queue,” is a data structure that allows the insertion and deletion of elements from both the front and back. This makes it versatile, as elements can be efficiently added or removed from either end.

The operations on a deque can be summarized as follows:

Deques are useful in situations where you need efficient insertion and deletion at both ends of the data structure. They can be employed in algorithms that require maintaining a sliding window of elements, palindrome checking, and other scenarios where elements need to be accessed from both ends.

2. What is BST? What are its applications?

A Binary Search Tree is a binary tree data structure where each node has at most two children, and for each node:

This ordering property ensures that a binary search can be efficiently performed on the tree. 

Key operations on a binary search tree:

    1. If the node to be deleted is a leaf (has no children), it can be removed directly.
    2. If the node has one child, the node is replaced by its child.
    3. If the node has two children, it is replaced by its in-order successor (or predecessor), and the in-order successor’s original position is adjusted.

Applications of Binary Search Trees

3. What are the ways to traverse a tree?

The three primary methods to traverse a tree are in-order, pre-order, and post-order traversal. 

4. Explain different types of queues

There are mainly four types of queues. They are

5. What are the representations of a graph data structure? What are the applications for graphs?

There are two main representations of graphs: the adjacency matrix and the adjacency list.

  1. An adjacency matrix is a 2D array (matrix) where each cell at position (a, b) represents whether there is an edge between vertex a and vertex b. If there is an edge, the cell contains a 1; otherwise, it contains a 0.
  2. An adjacency list is a collection of lists or arrays where each list represents the neighbors of a vertex. For each vertex v, the list contains all vertices adjacent to v.

Applications

Graphs are versatile data structures with a wide range of applications across various domains. Here are some common applications of graph data structures:

Learn More: Data Structure Learning Roadmap

6. What is the difference between BFS and DFS?

Breadth First Search (BFS) and Depth First Search (DFS) are two fundamental algorithms used to traverse and explore graphs or tree structures.

BFSDFS
Visits nodes level by level, starting from the source node. It explores all the neighbors of a node before moving on to the next level.Visits nodes branch by branch. Goes as deep as possible, exploring each branch before backtracking.
BFS uses a queue data structure. The first-in, first-out (FIFO) property ensures that nodes are processed in the order they are discovered.DFS uses a stack data structure. The last-in, first-out (LIFO) property ensures that nodes are explored deeply before backtracking.
Requires more memory compared to DFS because all the nodes at the current level need to be stored in the queue.
DFS requires less memory compared to BFS as it stores the nodes along the current branch.
BFS is used for shortest path finding, Web crawling, indexing, and peer-to-peer networks.DFS is used for topological sorting of graphs, Finding connected components, and Solving puzzles.

7. What are the different types of array data structures?

Arrays are data structures that store elements of the same type in contiguous memory locations. Here are some common types of array data structures:

           Example:

           [3 1 7 9]

            Example:

            [[1 3 4 5]

            [2 4 5 6]]

           Example:

           [[[1,2,3,4,5] 

           [6,7,8,9,1]]

           [[1,1,3,4,5]

           [6,7,1,0,2]]]

8. What are the different types of Linked List data structures?

Linked lists are linear data structures where elements are stored in nodes, and each node points to the next node in the sequence. Here are some common types of linked lists:

           0 -> 1 -> 2 -> 3 -> null

            null <- 0 <-> 1 <-> 2 <-> 3 -> null

            1 -> 2 -> 3 -> 1

            1 <-> 2 <-> 3 <-> 4 <-> 1

            ↑_______________________|

            In the example above, the arrows indicate the direction of the pointers. The last node points back to the first node, forming a circular structure in both directions.

9. What are infix and postfix expressions?

  1. Parentheses
  2. Exponents
  3. Multiplication and Division (from left to right)
  4. Addition and Subtraction (from left to right)

        Example:

        a + b * (c – d) / e

            Example:

            a b c d – * e /

Related Learning: Data Structures in C

10. Explain the concept of hashing and hash functions.

Hashing is the process of using a hash function to convert arbitrary-sized data to fixed-size values (hash codes). A hash function is a mathematical function that takes an input and returns a fixed-size string of characters, which is typically a hash code. The resulting hash code is often used as an index to locate a data record in a hash table quickly. The output appears random and unique for different inputs, and even a small change in the input should produce a significantly different hash value. Hashing is commonly employed in various applications, such as hash tables, digital signatures, data integrity checks, and cryptographic applications. A hash table is a data structure that uses hash functions to map keys to indexes in an array. Each index in the array corresponds to a “bucket” where the associated value is stored. Hash tables provide fast average-case time complexity for basic operations like insertion, deletion, and lookup.

Now, let’s move ahead with some common coding data structure interview questions.

Data Structure Coding Interview Questions

1. Given an array of integers, write a function to find the sum of all elements in Python.

#Function to find the sum of elements

def sum_func(array1):

    return sum(array1)

array1 = [1, 2, 3, 4, 5]

result = sum_func(array1)

print(f”The sum of elements in the array is: {result}”)

Output:

The sum of elements in the array is: 15

In this example, the function sum_func takes a list of integers as its parameter and uses the built-in sum_func function to calculate the sum of all elements in the array.

Related Learning: Data Structures in Python

2. Program to print the duplicate elements of an array.

#Initializing the array   

array = [1, 2, 3, 5, 9, 4, 5, 7, 6, 8, 0, 3, 0];   

print(“Duplicate elements in the array include: “); 

#Traversing through the elements using for Loop

for i in range(0, len(array)):  

    for j in range(i+1, len(array)):  

        if(array[i] == array[j]):  

            print(array[j]);  

Output:

   Duplicate elements in the array include: 

   3

   5

   0

3. Write a program to determine if two strings are anagrams

string1 = “Heart”;  

string2 = “Earth”;   

#Checking for the length of strings  

if(len(string1)!= len(string2)):  

    print (“The strings are not Anagram”);  

else:  

    #Using the lower() function to change the case of the string to lowercase

    string1 = string1.lower();  

    string2 = string2.lower();  

    #Sorting the strings  

    string1 = ”. join(sorted(string1));  

    string2 = ”. join(sorted(string2));  

      

    if (string1 == string2):  

        print (“The strings are Anagrams”);   

    else:  

        print (“The strings are not Anagrams”);  

Output:

The strings are Anagrams

4. Write a Program to count the number of vowels in a given string.

string = “Edureka”

# Function to count vowel

def vowels_count(string):

    temp = 0

    #All vowels 

    vowel = set(“aeiouAEIOU”)

    #For Loop to iterate through the string

    for element in string:

        if element in vowel:

            temp = temp + 1

    print(“Total number of vowels in the string:”, temp)

#Testing the Function 

vowels_count(string)

Output:

     Total number of vowels in the string: 4

5. Implement a stack using arrays.

class Stack:

    def __init__(self):

        self.items = []

    def is_empty(self):

        return len(self.items) == 0

    def push(self, item):

        self.items.append(item)

    def pop(self):

        if not self.is_empty():

            return self.items.pop()

        else:

            raise IndexError(“pop from an empty stack”)

    def peek(self):

        if not self.is_empty():

            return self.items[-1]

        else:

            raise IndexError(“peek from an empty stack”)

    def size(self):

        return len(self.items)

#Calling the function

stack1 = Stack()

stack1.push(1)

stack1.push(2)

stack1.push(3)

#Printing results

print(“Stack:”, stack1.items)

print(“Pop:”, stack1.pop())

print(“Peek:”, stack1.peek())

print(“Size:”, stack1.size())

The above example shows the implementation of basic stack operations like push, pop, and peek, checking if the stack is empty and getting the size of the stack. 

Output:

  Stack: [1, 2, 3]

  Pop: 3

  Peek: 2

  Size: 2

6. Write a program to calculate the height of a binary tree.

class Node:

    def __init__(self, value):

        self.value = value

        self.left = None

        self.right = None

def tree_height(root):

    if root is None:

        return 0

    else:

        left_height = tree_height(root.left)

        right_height = tree_height(root.right)

        # Return the maximum height of the left and right subtrees, plus 1 for the current level.

        return max(left_height, right_height) + 1

#Calling the function

root = Node(1)

root.left = Node(2)

root.right = Node(3)

root.left.left = Node(4)

root.left.right = Node(5)

tree_height = tree_height(root)

print(“Height of the binary tree:”, tree_height)

Output:

Height of the binary tree: 3

7. Implement a queue using an array.

class queue:

    # __init__ function

    def __init__(self, capacity):

        self.front = self.size = 0

        self.rear = capacity -1

        self.Q = [None]*capacity

        self.capacity = capacity

    # Checking if Queue is full

    def isfull(self):

        return self.size == self.capacity

    # Checking if Queue is empty

    def isempty(self):

        return self.size == 0

    # Function to add an element to the queue. 

    def enqueue(self, item):

        if self.isfull():

            print(“Full”)

            return

        self.rear = (self.rear + 1) % (self.capacity)

        self.Q[self.rear] = item

        self.size = self.size + 1

        print(“% s enqueued to queue” % str(item))

    # Function to remove an element from the queue. 

    def dequeue(self):

        if self.isempty():

            print(“Empty”)

            return

        print(“% s dequeued from queue” % str(self.Q[self.front]))

        self.front = (self.front + 1) % (self.capacity)

        self.size = self.size -1

    # Function to get front of queue

    def queuefront(self):

        if self.isempty():

            print(“Queue is empty”)

        print(“Front item is”, self.Q[self.front])

    # Function to get rear of the queue

    def queuerear(self):

        if self.isempty():

            print(“Queue is empty”)

        print(“Rear item is”, self.Q[self.rear])

# Adding elements to the queue

if __name__ == ‘__main__’:

    queue = queue(4)

    queue.enqueue(1)

    queue.enqueue(2)

    queue.enqueue(3)

    queue.enqueue(4)

    queue.dequeue()

    queue.queuefront()

    queue.queuerear()

Output:

      1 enqueued to queue

      2 enqueued to queue

      3 enqueued to queue

      4 enqueued to queue

      1 dequeued from queue

      Front item is 2

      Rear item is 4

In this example, the queue class represents a queue implemented using a circular array. The enqueue method adds an item to the rear end of the queue, the dequeue method removes and returns the item from the front end of the queue, and the peek method returns the item at the front without removing it. The queue keeps track of its front and rear indices to manage the circular nature of the array.

8. Write a Python program to reverse a linked list.

class node: 

    #Constructor 

    def __init__(self, data): 

        self.data = data 

        self.next_node = None

class linked_list: 

    # Initialize head 

    def __init__(self): 

        self.head_node = None

    # Function to reverse the linked list 

    def reverse_ll(self): 

        previous_node = None

        current_node = self.head_node

        while(current_node is not None): 

            next_node = current_node.next_node

            current_node.next_node = previous_node

            previous_node = current_node 

            current_node = next_node

        self.head_node = previous_node

    # Function to push a node 

    def push_node(self, new_data): 

        new_node = node(new_data) 

        new_node.next_node = self.head_node 

        self.head_node = new_node 

    # Printing the LinkedList 

    def print_ll(self): 

        count = self.head_node

        while(count): 

            print (count.data,end=” “) 

            count = count.next_node

# Testing the functions 

ll = linked_list() 

ll.push_node(2) 

ll.push_node(4) 

ll.push_node(1) 

ll.push_node(8) 

ll.push_node(3) 

print (“Linked List:”) 

ll.print_ll() 

ll.reverse_ll() 

print (“Reversed Linked List:”) 

ll.print_ll() 

Output:

   Linked List:

   3 8 1 4 2 

   Reversed Linked List

   2 4 1 8 3 

In the above example, the reverse_ll function takes the head node of the linked list as an input and reverses the links between nodes.

9. Write a Python Program to count the number of nodes in a complete Binary Tree.

class node:

    def __init__(self, data):

        self.left_node = None

        self.right_node = None

        self.data = data

# Function to get the total number of nodes in a complete binary tree

def totalnodes(root_node):

    if(root_node == None):

        return 0 

    # Find the left height and right height

    left_height = totalnodes(root_node.left_node)

    right_height = totalnodes(root_node.right_node)

    return 1 + left_height + right_height

# Function to create a new node

def newNode(data):

    Node = node(data)

    return Node

# Testing the function

root_node = newNode(1)

root_node.left_node = newNode(2)

root_node.right_node = newNode(3)

root_node.left_node.left_node = newNode(4)

root_node.left_node.right_node = newNode(5)

root_node.right_node.left_node = newNode(6)

print(“The total number of nodes in the tree is:”,totalnodes(root_node))

Output:

  The total number of nodes in the tree is: 6

10. Write a Python Program to find the length of the linked list:

class node:

    def __init__(self, data):

        self.data = data  

        self.next_node = None  

class Linked_List:

    # Initializing head node

    def __init__(self):

        self.head_node = None

    # Function to push a node to the Linked List

    def push(self, new_data):

        new_node = node(new_data)

        new_node.next_node = self.head_node

        self.head_node = new_node

    # Function to count the number of nodes in the Linked List

    def nodeCount(self):

        count = self.head_node # Initialise count

        nodecount = 0 # Initialise nodecount

        while (count):

            nodecount += 1

            count = count.next_node

        return nodecount

#Testing the function

if __name__ == ‘__main__’:

    ll = Linked_List()

    ll.push(9)

    ll.push(4)

    ll.push(1)

    ll.push(0)

    ll.push(2)

    ll.push(8)

    print(“The total count of nodes in the linked list is :”, ll.nodeCount())

Output:

The total count of nodes in the linked list is: 6

This brings us to the end of the ‘Data Structure Interview Questions’ blog. This blog covers the most common data structure interview questions through three sections- Interview Questions for freshers, Interview Questions for Experienced, and coding questions. I hope you are clear with all the three sections. Make sure to go through this article before going for the next interview. All the best!

Have a query for us? Kindly let us know in the comments section, and we’ll get in touch with you.

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