Top 30 Data Structure Interview Questions and Answers for 2025

Basic Data Structures Interview Questions

To demonstrate your understanding of basic data structures, you need to be very confident in core structures and their implementations. Questions like the following will test your ability to explain these ideas and show your knowledge.

What are the different types of data structures?

Data structures are classified as follows:

• Linear data structures: A data structure is considered linear if all its elements are arranged sequentially. In linear data structures, elements are stored in a non-hierarchical manner, where each item has a predecessor and a successor, except for the first and last elements.
• Non-linear data structures: A non-linear data structure does not form a sequence; rather, each item or element is connected to two or more other items in a non-linear arrangement. The data elements are not organized in a sequential structure.

Explain the difference between an array and a linked list.

Arrays and linked lists are two ways to store groups of items, but they work differently. Let’s see the main differences:

• Arrays. They act like a row of boxes in memory, allowing quick access to items by index, with a time complexity of O(1). However, adding or removing items from the middle is challenging because it requires shifting other items.
• Linked lists. They consist of nodes, where each node holds an item and points to the next one. This makes it easier to insert or delete items without affecting the whole list, but finding an item takes longer, with a time complexity of O(n).

What is a stack?

A stack is an ordered list where you can add or remove items at one end, known as the top. It is a recursive data structure that maintains a pointer to its top element. A stack is often referred to as a last-in-first-out (LIFO) list, meaning that the element added first will be the last one to be removed.

Stacks can be used for several applications, such as expression evaluation, backtracking, memory management, and function calls and returns.

How do you implement a stack using an array?

You can implement a stack using an array by taking advantage of the LIFO principle. Think of the array as a container, with one end acting as the top of the stack. 

When you want to add an item, you use the push operation to place it at the top. If you need to remove an item, you simply use the pop operation to take it off the top. 

In the following example, the push operation is implemented with the append() method in Python:

my_stack = []
item = 1
my_stack.append(item)
my_stack.pop()

By keeping track of the top's position with an index, you can make these operations quick and efficient.

Explain the concept of a queue and its common implementations in Python.

A queue is a first-in, first-out (FIFO) data structure, meaning the first element added is the first to be removed. You can think of it like a line at a store: people enter at the back and leave from the front.

In Python, you can implement a queue using different techniques:

• Using an array or list and taking advantage of the methods append() and pop():

my_queue = [] 
item = 1
# Enqueue
my_queue.append(item)
# Dequeue 
my_queue.pop(0)

• Using deque() from the collections library, which performs append() and pop() functions quicker than lists: 

from collections import deque
my_queue = deque()
item = 1
# Enqueue
my_queue.append(item)
# Dequeue 
my_queue.popleft()

• Using the in-built module queue.Queue:

from queue import Queue
my_queue = Queue(maxsize = 3)
# Enqueue
my_queue.put(item)
# Dequeue 
my_queue.get()

What is a binary search tree (BST), and how does it work?

A binary tree is a data structure where each node has at most two children: a left child and a right child. Then, a binary search tree (BST) is a specific type of binary tree that has distinct ordering properties:

• The left subtree of any node contains solely nodes with keys that are less than the key of that node.
• The right subtree of any node contains solely nodes with keys that exceed the key of that node.
• Both the left and right subtrees must also conform to the structure of binary search trees.

These properties facilitate efficient operations such as searching, insertion, and deletion, typically achieving a time complexity of O(log n) in balanced trees.

Binary search tree. Image by Author.

Explain the concept of hashing and its applications.

Hashing is a technique that takes data of any size and turns it into a fixed-size value called a hash value using a hash function. 

One common use of hashing is in hash tables, where it helps match keys with specific locations in an array, making it easy to find and retrieve data quickly. Hashing can have many applications, from helping secure passwords in cryptography to keeping data organized through deduplication.

What is a heap, and what are its common uses?

A heap is a data structure that resembles a tree and follows special rules. 

In a max-heap, the value of a parent node is always greater than or equal to the values of its children. In a min-heap, the parent’s value is smaller than or equal to its children's. 

Heaps are often used to create priority queues, which help sort items based on their importance or value. They are also important for heap sorting, which is a method of organizing data efficiently.

A min-heap is where all the parent nodes are smaller than the children—image by Author.

Intermediate Data Structures Interview Questions

Having covered the basics, let's move on to some intermediate-level data structure interview questions that explore your technical proficiency in implementing and using these fundamental concepts.

How would you balance a binary search tree?

A balanced binary search tree maintains a relatively equal height between its left and right subtrees. Balancing a BST is very important to maintain efficient search, insertion, and deletion operations. 

Techniques like AVL trees and red-black trees are commonly used to achieve self-balancing. AVL trees maintain a height difference of at most 1 between the left and right subtrees of any node, while red-black trees have stricter balance constraints.

How would you implement a min-heap in Python?

There are multiple approaches to solving this challenge. The following Python code demonstrates how I would implement a min-heap using a list.

Key operations include insertion, which adds an element while maintaining the min-heap property, and extraction of the minimum element, which removes the root and rearranges the tree to restore the min-heap property:

class MinHeap:
    def __init__(self):
        self.heap = [] 

    def __len__(self):  # Get the size of the heap
        return len(self.heap)

    def __parent(self, i):  # Get the parent index
        return (i - 1) // 2

    def __left(self, i):  # Get the left child index
        return 2 * i + 1

    def __right(self, i):  # Get the right child index
        return 2 * i + 2

    def __swap(self, i, j):  # Swap two elements
        self.heap[i], self.heap[j] = self.heap[j], self.heap[i]

    def __heapify_up(self, i):  # Restore min-heap property after insertion
        while i > 0 and self.heap[i] < self.heap[self.__parent(i)]:
            self.__swap(i, self.__parent(i))
            i = self.__parent(i)

    def __heapify_down(self, i):  # Restore min-heap property after extraction
        while True:
            smallest = i
            left = self.__left(i)
            right = self.__right(i)
            if left < len(self) and self.heap[left] < self.heap[smallest]:
                smallest = left
            if right < len(self) and self.heap[right] < self.heap[smallest]:
                smallest = right
            if smallest != i:
                self.__swap(i, smallest)
                i = smallest
            else:
                break

    def insert(self, val):  # Insert a value into the heap
        self.heap.append(val)
        self.__heapify_up(len(self) - 1)

    def extract_min(self):  # Extract the minimum value from the heap
        if not self.heap:
            return None
        min_val = self.heap[0]
        self.heap[0] = self.heap[-1]
        self.heap.pop()
        self.__heapify_down(0)
        return min_val

Explain the concept of a trie and its applications

A trie, also known as a prefix tree, is a tree-based data structure designed for efficient string retrieval and prefix matching. 

In a trie, each node represents a single character, and the paths from the root to the nodes correspond to complete strings. Tries are commonly used in various applications, such as autocomplete features, spell-checking tools, and the implementation of dictionaries.

A trie, where each node represents a single character that connects to form a string. Image By Author.

How would you implement a hash table with collision resolution?

In hash tables, a collision occurs when two different keys produce the same index. To solve this, you need to use a hash function to map keys to specific indices in an array. 

In my own experience, there are several methods for resolving collisions, including chaining, where colliding elements are stored in a linked list at the corresponding index, and open addressing, which involves finding the next available slot in the array through probing methods such as linear probing, quadratic probing, or double hashing.

Explain the concept of a graph and its different representations.

A graph is a data structure consisting of a collection of vertices, also known as nodes, interconnected by edges. This structure is useful for illustrating relationships and connections between various entities.

• Adjacency matrix. It is a way to represent a graph using a two-dimensional array. Each element in the array shows whether there’s an edge between two vertices. If you look at the row for vertex i and column for vertex j, the value there tells you if there’s a direct connection. A zero means there’s no connection, while a positive number shows the weight of that edge.
• Adjacency list. In this case, it uses a list of lists. Each index in the main list represents a vertex; the inner lists show which other vertices it’s directly connected to. This way of organizing the information is often more memory-efficient than the adjacency matrix, especially for sparse graphs, because it only keeps track of real connections instead of including every possible one.

How do you perform a depth-first search and breadth-first search on a graph?

Depth-first search (DFS) is an algorithm that explores a graph or tree by diving deep into each branch before backtracking. It can be implemented using an explicit stack or through recursion. The time complexity is O(V + E), where V is the number of vertices and E is the number of edges, meaning it may need to examine all vertices and edges.

Breadth-first search (BFS) systematically explores all nodes at the current depth level before moving to the next level. It is effective for finding the shortest path in unweighted graphs and is typically implemented using a queue. Like DFS, BFS has a time complexity of O(V + E), requiring a review of all vertices and edges.

Describe the trade-offs between different sorting algorithms.

Sorting algorithms are essential for efficient data processing by enabling faster searching, improved data analysis, and easier data visualization. When it comes to sorting algorithms, I can see important trade-offs to keep in mind:

• Bubble sort is simple, but it's really slow for large data structures since it has a time complexity of O(n^2).
• Merge sort does a much better job, running in O(n log n) time, but it needs some extra space because it relies on temporary arrays to piece everything back together.
• Quick sort usually works very well, and it is also running in O(n log n) time on average. But in the worst-case scenario, it will be slow with O(n^2) time if you pick incorrect pivot elements.

I leave you here some Python implementations:

# Bubble sort implementation
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

# Helper method for the quick sort implementation
def partition(arr, low, high):
    i = (low-1) 
    pivot = arr[high] 
    for j in range(low, high):
        if arr[j] <= pivot:
            i = i+1
            arr[i], arr[j] = arr[j], arr[i]
    arr[i+1], arr[high] = arr[high], arr[i+1]
    return (i+1)

# Quick sort implementation
def quick_sort(arr, low, high):
    if len(arr) == 1:
        return arr
    if low < high:
        pi = partition(arr, low, high)
        quick_sort(arr, low, pi-1)
        quick_sort(arr, pi+1, high)
    return arr

# Helper method for the merge sort implementation
def merge(left, right):
    if not left:
        return right
    if not right:
        return left
    if left[0] < right[0]:
        return [left[0]] + merge(left[1:], right)
    return [right[0]] + merge(left, right[1:])

# Merge sort implementation
def merge_sort(arr):
    if len(arr) <= 1:
        return arr
    mid = len(arr)//2
    left_half = merge_sort(arr[:mid])
    right_half = merge_sort(arr[mid:])
    return merge(left_half, right_half)

How would you approach the problem of finding the shortest path between two nodes in a graph?

Several algorithms can be used to find the shortest path in graphs. 

For unweighted graphs, breadth-first search effectively explores nodes layer by layer. In weighted graphs with non-negative edges, Dijkstra's algorithm identifies the shortest path by examining the nearest vertex first. 

The A* search algorithm improves efficiency by using heuristics to estimate remaining costs. The choice of algorithm depends on the graph's characteristics and the specific problem requirements.

Advanced-Data Structures Interview Questions

Let's explore some advanced interview questions for those seeking more senior roles or aiming to demonstrate a deep knowledge of specialized or complex data structures.

Explain the concept of dynamic programming and how it can be applied to solve problems involving data structures.

Dynamic programming is a method used to solve complex problems by dividing them into smaller overlapping subproblems. Instead of starting from scratch each time, you keep track of the solutions to those smaller parts, which means you don’t have to do the same calculations repeatedly. 

This method is very useful for finding the longest common subsequence between two strings or finding the minimum cost to reach a specific point on a grid. 

Explain the concept of a B-tree and its advantages over a binary search tree.

B-trees are balanced tree data structures designed for efficient disk access. Some of its features are:

• All leaves have the same depth.
• Each node holds a variable number of keys within a specified range.
• Internal nodes act as index structures that direct searches to the appropriate subtree.

They offer several advantages over binary search trees:

• Reduced disk I/O: Multiple keys can be stored per node, minimizing the number of disk reads needed to locate a specific key.
• Improved performance: For larger datasets, its ability to handle more keys per node results in fewer levels in the tree and faster searches.

Describe the concept of topological sorting and its applications.

Topological sorting is an algorithm used for ordering the vertices of a directed acyclic graph (DAG) such that if there is an edge from vertex u to vertex v, then u appears before v in the order. 

This algorithm can be used in various applications, one of the most common being task scheduling, where it helps determine the sequence of tasks that need to be performed in a project. I wrote about this topic in my in-depth blog post about directed acyclic graphs. 

Describe the difference between a min-heap and a priority queue.

A min-heap is a specific implementation of a priority queue and is defined as a complete binary tree where the value of each node is less than or equal to the values of its children, allowing for efficient operations when finding and extracting the minimum element. 

On the other hand, a priority queue is an abstract data structure that permits the insertion of elements with an associated priority, with elements being dequeued in order of their priority. Min-heaps are a common way to implement priority queues due to their ability to manage these operations efficiently.

Explain the concept of a disjoint-set data structure and its applications.

A disjoint-set data structure, also known as a union-find data structure, maintains a collection of disjoint sets.  This data structure supports two primary operations: 

• Find: Determines which set a particular element belongs to.
• Union: Merges two sets into a single set. 

There are many applications of disjoint datasets, but to my knowledge, the most common ones are Kruskal's algorithm for finding the minimum spanning tree of a graph and the network flow problem for determining connected components within a graph.

Explain the concept of a segment tree and its applications.

A segment tree is a data structure designed to facilitate efficient range queries and updates on an array. It is particularly useful for scenarios where we need to repeatedly perform operations such as finding the sum, minimum, maximum, or greatest common divisor over a specific range of elements in the array. 

It is constructed as a binary tree, where each node represents a segment of the array. The leaves of the tree correspond to individual elements of the array, while internal nodes store information that aggregates the values of their child nodes according to the operation being performed. They achieve O(log n) time complexity for both updates and queries.

How would you implement a suffix tree?

A suffix tree is a useful data structure that stores all the suffixes of a string in a space-efficient way. It makes searching through strings quick and easy. 

Building a suffix tree usually involves adding the suffixes one at a time, but some techniques, such as using suffix links, help speed up the process. 

Here, I leave you a Python implementation:

class SuffixTreeNode:
    def __init__(self):
        self.children = {}  # Dictionary to store child nodes
        self.start = 0  # Starting index of the substring represented by the edge
        self.end = 0  # Ending index of the substring represented by the edge

class SuffixTree:
    def __init__(self, text):
        self.root = SuffixTreeNode()
        self.text = text + "$"  # Append a special character to mark the end

    def insert_suffix(self, index):
        node = self.root
        i = index
        while i < len(self.text):
            c = self.text[i]
            if c not in node.children:
                # Create a new child node
                new_node = SuffixTreeNode()
                new_node.start = i
                new_node.end = len(self.text) - 1 
                node.children[c] = new_node
            node = node.children[c]
            i += 1

    def build_tree(self):
        """
        Builds the suffix tree for the given text.
        """
        for i in range(len(self.text)):
            self.insert_suffix(i)

What are quadtrees, and which are their most common applications?

Quadtrees are a hierarchical tree data structure that recursively subdivides a two-dimensional space into four equal quadrants. This spatial partitioning technique is highly effective for applications like image processing, collision detection in games, and geographic information systems for efficient spatial data storage and retrieval.

Scenario-Based Data Structures Interview Questions

Demonstrating your data structure knowledge is important, but showcasing that you know when to use them properly will make you stand out in your interview. In this section, we’ll review how to apply your data structure knowledge to practical situations.

Imagine that you are designing a system for a ride-sharing service. Which data structure will you use to match drivers with riders in real time?

Due to the problem's real-time nature, this challenge will require efficient data structures. 

In my experience, I’d use quadtrees for geographical data, priority queues to rank potential matches based on distance and rider urgency, and hash tables for efficient lookups of driver and rider locations.

What data structure will you use to recommend products to users based on their past behavior?

We can leverage a combination of data structures to effectively recommend products based on user behavior. 

A sparse user-item matrix would store user-product interactions, while hash tables would efficiently map users and items. Priority queues would rank recommendations, and graph structures could model user-item relationships for more sophisticated analyses like community detection. 

You are designing a system for a social networking platform. What data structure can help you detect and remove spam accounts?

A graph data structure can be highly effective for detecting and removing spam accounts on a social networking platform. You can analyze the network topology by representing users as nodes and their connections as edges. Identifying densely connected clusters, isolated nodes, and sudden spikes in activity can help flag suspicious accounts.

What data structures would you use to deliver messages to the correct recipients on a real-time chat application?

I would use a combination of data structures in a real-time chat application. 

Hash tables would store user IDs and their corresponding connection lists, enabling quick lookups of users to send messages to. Queues would be implemented for each user to maintain the order of messages, ensuring they are delivered in the sequence they were sent. Additionally, trees, such as AVL trees, could be used to efficiently store and retrieve users' online/offline status, allowing for real-time updates on user availability.

You are building a spell checker for a word-processing application. What data structures would you use to store and search for valid words in a dictionary efficiently?

For a spell checker, efficient word lookup is very important. A trie would be an ideal data structure. Each node in the trie would represent a letter, and paths through the trie would form words. This allows for fast prefix-based searches, enabling the spell checker to suggest corrections for misspelled words quickly.

What data structure would you use to design a system for a real-time strategy game that efficiently handles area queries for structures and updates for new buildings?

In this particular scenario, segment trees stand out as an excellent choice. They are very good at handling range queries and updates efficiently. We can represent the game map as a 1D array, where each element corresponds to a grid cell. Each cell can store information about the presence or absence of a structure.

Tips for Preparing for a Data Structures Interview

I know that preparing for a data structures interview can be challenging, but a structured approach can help you make it more manageable!

Focus on mastering the fundamental concepts behind data structures, such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand their principles, how they manage data, and the time complexities associated with operations like insertion, deletion, and search.

Knowing the concepts is good but not enough. You should know how to implement these data structures from scratch. You can engage with DataCamp courses to take advantage of coding challenges that sharpen your problem-solving skills. 

Understanding the trade-offs between data structures is key. For example, arrays allow quick access but can be costly for insertions and deletions, while linked lists offer efficient modifications but require traversal for access. Be prepared to discuss these trade-offs during your interview.

Finally, connect your knowledge to real-world applications. Consider how you could use data structures, such as those we have explored in this article, in web development, database systems, or machine learning.

Conclusion

In this article, we've covered many data structure interview questions spanning basic, intermediate, and advanced topics. From understanding the core concepts of data structures like arrays, linked lists, stacks, and queues to diving into more complex graph and hash table techniques, we've explored the key areas that potential employers might inquire about.

If you need more data structure training for your interview, check out the following courses and blogs:

• Introduction to Data
• Data Structures and Algorithms in Python
• Data Structures: A Comprehensive Guide With Python Examples