Cracking the Coding Interview with 65 Coding Questions in 2025
Updated on Mar 28, 2025 | 47 min read | 46.0k views
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Updated on Mar 28, 2025 | 47 min read | 46.0k views
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Did you know? The demand for coders with skills in Python, JavaScript, and Java is very high – around 40% of recruiters look for coders with these skills. Preparing for coding interviews can, thus, be a game-changer in shaping your career.
Mastering basic coding questions can speed up the process of cracking the coding interview, building your career in software engineering and development, data science, and cutting-edge technology fields.
This guide is designed to help you tackle coding challenges across all levels, from beginner-friendly basic coding questions for interviews to advanced scenarios. By focusing on critical concepts, practical examples, and interview-focused exercises, you’ll improve your technical skills and build the confidence to ace technical interviews.
This section’s basic coding questions are ideal for beginners and entry-level candidates — fresh graduates, coding bootcamp alumni, or anyone looking to strengthen their foundational knowledge.
If you’re new to programming interviews or returning to the field after a long gap, these questions will help you in cracking the coding interview by building a solid bedrock of skills and confidence.
By working through these 26 essential coding questions for placement, you’ll hone the following skills:
You can also check out this free upGrad’s video on mastering coding questions for interviews across multiple programming languages.
Now, let’s explore all 26 coding interview questions with solutions in detail.
Why Do Interviewers Ask This Question?
They want to assess your foundational knowledge of how data is stored and organized. Showing you understand different structures (like arrays, stacks, queues) indicates you can pick the right tool for various programming tasks.
Sample Answer
A data structure organizes and stores data efficiently, enabling quick operations like searching and updating. It’s vital for optimizing algorithms and solving practical problems.
Here’s why data structures are essential.
Example: Think of an array as a bookshelf where each slot holds a book. You can instantly grab a book by knowing its position, just as you can access an array element using its index.
Why Do Interviewers Ask This Question?
It’s one of the most basic coding round questions for freshers to check if they understand contiguous memory storage and indexing. Demonstrating array usage and common operations shows you can handle fundamental data manipulation efficiently.
Sample Answer
An array stores elements of the same type in contiguous memory, enabling efficient fixed-size data storage and manipulation.
Here’s how you can further explain their significance.
Example: Imagine building an app to store temperatures recorded daily. Arrays allow storing all the temperature readings for a week in a single variable, making it simple to retrieve, modify, or analyze the data.
Why Do Interviewers Ask This Question?
This is one of the basic coding questions for interviews that often comes up to test your understanding of dynamic data structures and how well you can explain a core concept.
Sample Answer
A linked list is a dynamic data structure where elements, called nodes, are connected using pointers. Each node contains two parts.
Example: Let’s say you’re building a to-do list app. Tasks can be added or removed at any time, and their order might change dynamically. A linked list is ideal here because it allows efficient operations without shifting elements like an array.
Here are some applications of a linked list.
Feature | Description |
Task scheduling | Used to maintain an ordered list of tasks that can be modified dynamically. |
Undo/redo functionality | Found in text editors or design tools, tracking changes made over time. |
Why Do Interviewers Ask This Question?
It’s asked to test your understanding of arrays and linked lists, focusing on their differences in structure and use cases.
Sample Answer
Arrays store elements in contiguous memory locations, enabling quick random access via an index. Linked lists, however, consist of nodes connected by pointers, with each node holding data and a reference to the next.
Here’s how they differ: arrays vs linked lists.
Feature | Arrays | Linked Lists |
Structure | Arrays are faster for random access. | Linked lists allow flexible non-contiguous storage. |
Size | Arrays have a fixed size. | Linked lists grow dynamically. |
Insertion/Deletion | Slower in arrays due to shifting elements. | Faster in linked lists by adjusting pointers. |
Memory Usage | Arrays are efficient for fixed data. | Linked lists require extra memory for pointers. |
Example: If you’re implementing a student attendance tracker:
Code Snippet and Explanation:
This code compares arrays and linked lists for managing student lists.
# Example 1: Using an Array
students_array = ["Sneha", "Dinesh", "Arup"]
# Accessing the second student
print("Array: Second student:", students_array[1])
# Adding a new student
students_array.append("Pooja")
print("Array: Updated list:", students_array)
# Example 2: Using a Linked List
class Node:
def __init__(self, student):
self.student = student
self.next = None
# Creating a linked list of students
student1 = Node("Sneha")
student2 = Node("Dinesh")
student3 = Node("Arup")
# Linking the students
student1.next = student2
student2.next = student3
# Adding a new student
new_student = Node("Pooja")
student3.next = new_student
# Traversing the linked list
print("Linked List: Student List:")
current_student = student1
while current_student:
print(current_student.student)
current_student = current_student.next
Input:
["Sneha", "Dinesh", "Arup"]
Sneha -> Dinesh -> Arup -> Pooja
Output:
Array: Second student: Dinesh
Array: Updated list: ['Sneha', 'Dinesh', 'Arup', 'Pooja']
Linked List: Student List:
Sneha
Dinesh
Arup
Pooja
Also Read: 50+ Programming Interview Questions to Succeed in 2025
Why Do Interviewers Ask This Question?
Cracking the coding interview for beginners often hinges on how you tackle questions like these. It’s asked to see if you understand programming paradigms beyond procedural code. Being fluent in OOP principles (classes, objects, encapsulation) is vital for scalable software design.
Sample Answer
OOP organizes code into objects, instances of classes that encapsulate data (attributes) and behavior (methods). It enhances modularity, reusability, and maintainability by modeling real-world systems.
The primary purpose of OOP is to make code more modular, reusable, and easier to maintain by modeling real-world systems.
Example: Imagine you’re designing a system for an online store:
Also Read: What are the Advantages of Object-Oriented Programming?
Why Do Interviewers Ask This Question?
They want to confirm you can map real-world concepts into code. Understanding classes as blueprints and objects as instances demonstrates how you architect OOP-based solutions.
Sample Answer
Here’s a clear-cut definition of both:
Example: In a student management system, a class defines attributes like name and age and methods like updating marks. Each object represents an individual student with specific data.
Why Do Interviewers Ask This Question?
It’s one of the most critical coding questions employers ask to check if you can apply it practically to reduce duplication and create well-structured code.
Sample Answer
Inheritance allows a class (child) to derive properties and behavior from another class (parent). The parent class provides common functionality, while the child class adds or overrides attributes or methods.
Example Code Snippet and Explanation:
This code demonstrates inheritance with a Vehicle parent class (shared attributes like brand, model, and start_engine method).
# Parent class
class Vehicle:
def __init__(self, brand, model):
self.brand = brand
self.model = model
def start_engine(self):
return f"{self.brand} {self.model}: Engine started!"
# Child class inheriting from Vehicle
class Car(Vehicle):
def __init__(self, brand, model, airbags):
super().__init__(brand, model) # Reusing the parent class constructor
self.airbags = airbags # Specific to Car
# Overriding the start_engine method
def start_engine(self):
return f"{self.brand} {self.model}: Engine started with advanced features!"
# Additional method
def safety_features(self):
return f"{self.brand} {self.model} has {self.airbags} airbags."
# Child class inheriting from Vehicle
class Motorcycle(Vehicle):
def __init__(self, brand, model, type_of_handlebars):
super().__init__(brand, model)
self.type_of_handlebars = type_of_handlebars # Specific to Motorcycle
# Creating objects
car = Car("Toyota", "Camry", 6)
motorcycle = Motorcycle("Harley-Davidson", "Street 750", "Cruiser")
# Using methods
print(car.start_engine()) # Output: Toyota Camry: Engine started with advanced features!
print(car.safety_features()) # Output: Toyota Camry has 6 airbags.
print(motorcycle.start_engine()) # Output: Harley-Davidson Street 750: Engine started!
Input:
Output:
Toyota Camry: Engine started with advanced features!
Toyota Camry has 6 airbags.
Harley-Davidson Street 750: Engine started!
Harley-Davidson Street 750 has Cruiser handlebars.
Also Read: Types of Inheritance in Java: Key Concepts, Benefits and Tips to Master Inheritance
Why Do Interviewers Ask This Question?
This is one of those basic coding questions that evaluate how you explain the ability of a single method or interface to adapt to different scenarios.
Sample Answer
Polymorphism in OOP allows a method to perform different tasks based on the object calling it, enabling seamless interaction with various object types.
It is important because of the following reasons.
Example: Think of a “perform” button on different types of devices.
The button is the same (shared interface), but its behavior depends on the device (object) you’re interacting with.
Why Do Interviewers Ask This Question?
They’re testing your familiarity with access modifiers and class types. This shows how well you understand encapsulation, security of data, and when to use utility (static) classes.
Sample Answer
Here’s how the three differ:
Example: A public class is like a library (open to all), a private class is like a personal notebook, and a static class is like a toolbox for instant use.
Why Do Interviewers Ask This Question?
Loops are among the most fundamental constructs. Employers check if you can automate repetitive tasks and understand the nuances between for, while, and do-while loops.
Sample Answer
A loop repeats code based on a condition, optimizing tasks like processing lists or performing calculations efficiently.
Different Types of Loops
Loop Type | What Is It? | Example |
For Loop | Executes a block of code a specific number of times. | Iterating through a list of numbers. |
While Loop | Repeats a block of code as long as a condition is true. | Continuously reading data from a file until the end. |
Do-While Loop | Executes a block of code at least once, even if the condition is false, because the condition is checked after execution. | Asking a user to input a number and displaying it, even if they input an invalid number the first time. |
Why Do Interviewers Ask This Question?
It’s one of the most basic coding questions asked to see if you can control program flow logically. Knowing if-else structures indicates comfort with decision-making processes in code.
Sample Answer
Conditional statements execute specific code blocks based on conditions, like if statements for checking seat availability.
Here are the main types of conditional statements:
Here are the examples for each type:
if Statement Example: Checking if seats are available.
Code Snippet and Explanation:
This code checks if the number of available seats is greater than 0. If the condition is true, it prints that seats are available. This is a simple condition with one outcome.
seats_available = 5
if seats_available > 0:
print("Seats are available!")
if-else Statement Example: Informing customers when seats are unavailable.
Code Snippet and Explanation:
Here, the code evaluates whether seats are available. If seats are greater than 0, it informs customers that seats are available. Otherwise, it prints a message saying no seats are available.
seats_available = 0
if seats_available > 0:
print("Seats are available!")
else:
print("Sorry, no seats are available.")
if-elif-else Statement Example: Prioritizing customers with special needs while managing seat availability.
Code Snippet and Explanation:
This snippet handles multiple conditions.
special_needs = True
seats_available = 0
if seats_available > 0:
print("Seats are available!")
elif special_needs:
print("Prioritizing customers with special needs.")
else:
print("Sorry, no seats are available.")
Also Read: Conditional Statements in C Programming
Why Do Interviewers Ask This Question?
Loop implementation questions verify your ability to iterate in different ways. Being clear about syntax and use cases shows you can handle repetitive tasks effectively.
Sample Answer
A For loop is best for iterating over a known sequence, like a list or range of numbers. A While loop, on the other hand, is used when the number of iterations depends on a condition.
Example:
Why Do Interviewers Ask This Question?
This is one of the most commonly asked coding interview questions to check your grasp of base cases, stack usage, and when to prefer iterative solutions. Recursion indicates problem-solving skills for tasks that break down into smaller subproblems, and that’s what interviewers are looking for.
Sample Answer
Recursion occurs when a function calls itself to solve smaller instances of a problem, stopping when a base condition is met.
Here is how it works:
Example: Imagine counting down from 5 to 1. Instead of writing separate instructions for each number, a recursive function does this by repeatedly calling itself to count the next number.
Also Read: Recursion in Data Structure: How Does it Work, Types & When Used
Why Do Interviewers Ask This Question?
They test basic string manipulation to see if you can implement simple logic. It also reveals if you know language-specific methods or manual approaches (like looping backward).
Sample Answer
To reverse a string, iterate through it backward or use built-in functions.
Example Code Snippet and Explanation:
This code reverses the string "hello" by using Python’s slicing feature ([::-1]), which starts from the end and moves backward to create a reversed version of the string. It then prints the reversed string.
s = "hello"
reversed_s = s[::-1]
print(reversed_s) # Output: "olleh"
Input:
s = "hello"
Output:
"olleh"
Why Do Interviewers Ask This Question?
This is one of the most basic coding questions for interviews that’s asked to test your ability to compare strings or use two-pointer techniques. It’s also a neat way to see if you handle edge cases (e.g., empty strings).
Sample Answer:
A string is a palindrome if it reads the same forwards and backwards. Compare the string with its reverse.
Why Do Interviewers Ask This Question?
Interviewers gauge your basic string traversal, conditional checks, and the idea of classifying characters. It’s a straightforward logic exercise that reveals debugging and counting skills.
Sample Answer
Iterate through the string and count vowels and consonants using conditions.
Example Code Snippet and Explanation:
This code counts vowels and consonants in the string "hello world" by iterating through each character. It checks whether a character is a vowel or consonant using conditions and sums them up separately.
s = "hello world"
vowels = "aeiou"
vowel_count = sum(1 for char in s if char.lower() in vowels)
consonant_count = sum(1 for char in s if char.isalpha() and char.lower() not in vowels)
print(f"Vowels: {vowel_count}, Consonants: {consonant_count}") # Output: Vowels: 3, Consonants: 7
Input:
s = "hello world"
Output:
Vowels: 3, Consonants: 7
Why Do Interviewers Ask This Question?
This is one of those basic coding questions that test simple array traversal and comparison logic. It also shows familiarity with built-in functions or manual iteration techniques.
Sample Answer
Iterate through the array to find the largest element or use a built-in function.
Example Code Snippet and Explanation:
This code sorts the array in ascending order using sorted() and selects the last element (largest) with [-1]. It then prints the largest value, which is 10 in this case.
arr = [7, 2, 10, 4, 6]
max_element = sorted(arr)[-1] # Using sorting to find the maximum
print(max_element) # Output: 10
Input:
arr = [7, 2, 10, 4, 6]
Output:
10 # The largest element in the array
Why Do Interviewers Ask This Question?
Sorting is key for many real-world problems. They check if you know built-in methods or can implement basic sorting algorithms (like Bubble Sort) under time constraints.
Sample Answer
Use built-in sorting functions or implement a sorting algorithm like Bubble Sort.
Example Code Snippet and Explanation:
This code sorts the array [5, 3, 8, 1, 9] in ascending order using Python’s built-in sorted() function. It creates a new array with the elements arranged from smallest to largest and prints the result.
arr = [5, 3, 8, 1, 9]
sorted_arr = sorted(arr)
print(sorted_arr) # Output: [1, 3, 5, 8, 9]
Input:
arr = [5, 3, 8, 1, 9]
Output:
[1, 3, 5, 8, 9]
Why Do Interviewers Ask This Question?
It’s one of the most asked coding questions for placement that tests your string manipulation capabilities and comparison logic. Interviewers check if you can efficiently identify when two strings share the same characters in different orders.
Sample Answer
An anagram is a word formed by rearranging the letters of another, like "listen" and "silent." To find anagrams of a string, sort the characters of the string and compare it with the sorted characters of other strings.
Example: To check if "listen" and "silent" are anagrams, here’s what’s done:
Also Read: Anagram Program in Python | Methods and Examples
Why Do Interviewers Ask This Question?
They want to see if you know different strategies — like using a set or two-pointer technique. This also highlights your ability to discuss time and space complexity trade-offs.
Sample Answer
There are three main methods to remove duplicates.
Why Do Interviewers Ask This Question?
Cracking the coding interview hinges on questions such as these. Employers often want you to handle edge cases (duplicates, small arrays). Your approach reveals whether you can optimize or rely on simple sort-and-pick methods.
Sample Answer
To find the second largest number, you need to follow two steps:
Code Snippet and Explanation:
This code finds the largest number in the array (5) and then checks the remaining numbers to find the largest one smaller than 5, which is 4. It ignores duplicates of the largest number during the process.
arr = [5, 3, 1, 4, 5]
largest = max(arr) # Find the largest number
second_largest = float('-inf') # Initialize as the smallest possible value
for num in arr:
if num != largest and num > second_largest:
second_largest = num # Update second largest
print(second_largest) # Output: 4
Input:
# Input array
arr = [5, 3, 1, 4, 5]
Output:
4
This is one of the most critical coding round questions for freshers that checks your in-place manipulation skills. Interviewers want to see if you understand swapping elements and can manage memory constraints.
Sample Answer
Arrays can be reversed by swapping elements from the start with those at the end until you reach the middle.
Example: Imagine a line of people where the first swaps with the last, the second swaps with the second-last, and so on, until the order is completely reversed. This process happens directly without moving them to a new location, just like swapping elements in an array.
It’s one of the leading coding questions asked in interviews to test your logic and loop usage. It often opens up discussions on performance optimization for large inputs (checking divisors up to √n).
Sample Answer
A prime number is greater than 1 and divisible only by 1 and itself. To check if a number is prime, test if it’s divisible by any number from 2 to the square root of the number.
Example Code Snippet and Explanation:
The code checks if the number is divisible by any smaller number (starting from 2 up to the square root of the number).
def is_prime(num):
if num <= 1:
return False
for i in range(2, int(num ** 0.5) + 1):
if num % i == 0:
return False
return True
print(is_prime(7)) # Output: True
Input:
num = 7 # Check if the number 7 is prime
Output:
True # 7 is a prime number
Factorials test both looping and recursive thinking. Employers use it to confirm you understand function calls, base cases, and iterative vs. recursive trade-offs.
Sample Answer
The factorial of a number is the product of all integers from 1 to that number.
It can be calculated using two ways:
Example: For 5! = 5 × 4 × 3 × 2 × 1 = 120.
Iteration Code Snippet and Explanation:
The iterative function uses a loop to multiply all numbers from 1 to n step by step.
def factorial_iterative(n):
result = 1
for i in range(1, n + 1):
result *= i
return result
print(factorial_iterative(5)) # Output: 120
Input:
n = 5 # Find the factorial of 5
Output:
120 # Factorial of 5 is 5 * 4 * 3 * 2 * 1
Recursion Code Snippet and Explanation:
The recursive function breaks the problem into smaller pieces by multiplying the current number (n) with the factorial of the previous number (n-1).
def factorial_recursive(n):
if n == 1:
return 1
return n * factorial_recursive(n - 1)
print(factorial_recursive(5)) # Output: 120
Input:
n = 5 # Find the factorial of 5
Output:
120 # Factorial of 5 is 5 * 4 * 3 * 2 * 1
It’s one of the most commonly asked coding questions that assess your understanding of stack operations — push and pop. LIFO questions ensure you grasp this fundamental approach to structured data handling.
Sample Answer
LIFO (Last In, First Out) means the last element added to a stack is removed first, like a stack of plates where the top plate is removed first.
Example: A stack can be implemented in programming using arrays or linked lists. Here are the common operations to implement stack in a data structure:
Queues are crucial in scheduling and real-time data processing. Employers want to see if you know queue operations (enqueue, dequeue) and can explain real-world use cases.
Sample Answer
A queue is a linear data structure where the first element added is the first removed, like a ticket line.
Queues can be implemented using arrays and linked lists.
Example:
These advanced coding questions are designed for developers with a solid grasp of programming fundamentals — typically those with a few projects under their belt or 1-4 (or higher) years of practical experience.
If you’re at the stage where you’re ready to tackle more complex data structures, optimize performance, or demonstrate in-depth algorithmic thinking, these 33 coding interview questions will help you showcase and refine those higher-level capabilities.
Here are the key skills you can hone through these questions:
Now, let’s explore the most asked coding interview questions with solutions to help you in cracking the coding interview.
Employers want to see if you understand hierarchical data structures and how to apply them in searches, retrievals, or organizing nested information — vital in many real-world systems like file directories.
Sample Answer
A binary tree is a hierarchical structure where each node has up to two children (left and right). It is used for efficient searching, sorting, and representing hierarchical data.
Here are the key uses:
It’s one of those advanced coding questions that’s asked to test your ability to maintain ordered data and perform fast lookups or insertions. Understanding BST logic (left < node < right) shows you grasp critical algorithmic trade-offs.
Sample Answer
A Binary Search Tree (BST) is a type of binary tree where:
Here’s how they work.
This continues until the value is found or the search terminates at a leaf node.
Example: Imagine you are building a contact list. A BST helps organize the contacts alphabetically, so when you search for a name, you only check relevant branches rather than scanning the entire list.
They want to confirm you can classify and select the right data structure (e.g., arrays vs. graphs) depending on whether you need sequential or hierarchical/graph-based organization.
Sample Answer
Linear data structures store elements sequentially, like arrays and linked lists. Non-linear data structures, on the other hand, represent hierarchical relationships, like trees and graphs.
The key difference between linear and non-linear data structures is that linear structures are ideal for simpler, ordered data. Non-linear structures, on the flip side, handle complex relationships and large datasets efficiently.
Example:
It’s one of those coding interview questions that checks your grasp of fundamental sorting logic, nested loops, and talking through time complexity.
Sample Answer
Bubble sort repeatedly compares adjacent elements in an array, swapping them if they are in the wrong order. This process continues until the array is sorted. It’s called "bubble" sort because smaller elements “bubble” up to the top of the array in each pass.
Example Code Snippet and Explanation:
This code sorts a list by repeatedly comparing two adjacent numbers and swapping them if they are in the wrong order.
def bubble_sort(arr):
n = len(arr)
for i in range(n): # Pass through the array
for j in range(0, n - i - 1): # Compare adjacent elements
if arr[j] > arr[j + 1]: # Swap if out of order
arr[j], arr[j + 1] = arr[j + 1], arr[j]
# Example input
numbers = [5, 3, 8, 4]
bubble_sort(numbers)
print(numbers) # Output:
Input:
# Input list of numbers to sort
numbers = [5, 3, 8, 4]
Output:
# Output after sorting
[3, 4, 5, 8]
Explore the basics of the Bubble Sort Algorithm with upGrad’s free tutorial. Learn how to write one from scratch.
Insertion sort coding questions reveal whether you know how to handle partially sorted data and can articulate how inner and outer loops move elements into position.
Sample Answer
Insertion sort builds the sorted array one element at a time by comparing each new element with the sorted part of the array and placing it in the correct position.
Example: Imagine you're sorting playing cards in your hand.
They want to test your ability to apply divide-and-conquer. Knowing edge cases (like mid calculation) and complexity (O(log n)) indicates robust problem-solving.
Sample Answer
Binary search repeatedly divides the search interval in half. It compares the middle element of a sorted array to the target value.
This continues until the value is found or the interval is empty.
Example: Think of guessing a number between 1 and 100. Instead of guessing randomly, you keep halving the range.
This is exactly how binary search works!
It’s asked to check if you can weigh different algorithms — like Merge Sort vs Quick Sort — and discuss performance trade-offs in practical scenarios.
Sample Answer
There is no single "best" sorting algorithm — it depends on the dataset and requirements.
For general-purpose sorting, Merge Sort and Quick Sort are often considered efficient due to their average-case time complexity of O(nlogn)O(n \log n)O(nlogn).
For nearly sorted data, Insertion Sort or Bubble Sort may perform better due to their simplicity.
Why Do Interviewers Ask This Question?
This is one of those coding questions for interviews that shows you can handle string manipulation efficiently (sorting or hashing). This also reveals how you think about matching character frequency and order.
Sample Answer
The Fibonacci sequence is a series where each number is the sum of the two preceding ones. Using recursion, the function calls itself to calculate each term based on the previous two terms.
Example: To print the first 5 Fibonacci numbers:
Code Snippet and Explanation:
This code calculates each Fibonacci number by calling the Fibonacci function recursively.
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
# Print the first 5 Fibonacci numbers
for i in range(5):
print(fibonacci(i), end=" ") # Output: 0 1 1 2 3
Input:
n = 5 # Generate the first 5 terms of the Fibonacci sequence
Output:
0 1 1 2 3
Also, see this free upGrad tutorial on Fibonacci Series in Python.
Why Do Interviewers Ask This Question?
A classic string problem that tests sliding-window or two-pointer techniques. It shows your ability to handle dynamic constraints and track characters efficiently.
Sample Answer
To find the longest substring without repeating characters, use a sliding window approach. Track characters in the current substring and update the maximum length when the window expands or shrinks.
Example: For the string "abcabcbb", the longest substring without repetition is "abc" (length 3).
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Why Do Interviewers Ask This Question?
It’s one of those coding questions asked in interviews to gauge your knowledge of O(1) average lookups via hashing. Understanding collisions, load factor, and use-cases like caching or frequency counting is critical in modern development.
Sample Answer
A hashmap is a data structure that stores key-value pairs and allows fast access to values using keys. It uses a hash function to map keys to specific indices in an array. Hashmaps are highly efficient for operations like search, insert, and delete, often achieving O(1)O(1)O(1) time complexity.
Applications of Hashmaps:
Example: Hashmaps are used in programming dictionaries, where you store words (keys) and their definitions (values).
Why Do Interviewers Ask This Question?
It’s asked to check if you know adjacency structures, traversal algorithms (DFS/BFS), and real-world scenarios where graphs excel.
Sample Answer
A graph is a collection of nodes (vertices) connected by edges, representing relationships between entities. Graphs can be directed (edges have direction) or undirected (no direction).
It’s used in programming in these key ways:
Example: Graphs are used in social networks like Facebook, where nodes represent users and edges represent friendships or connections.
Why Do Interviewers Ask This Question?
It’s one of those coding questions that digs into pointer manipulation and node structure. It also checks if you grasp trade-offs — like constant-time insertions vs potential overhead when you need backward traversal.
Sample Answer
Here’s a straightforward answer:
Examples of When to Use Them:
Also Read: Mastering Linked Lists in Data Structure
Why Do Interviewers Ask This Question?
Employers want to see if you can define node classes, write insert/find logic, and maintain BST ordering. Coding this well underlines your comfort with recursion or iterative tree traversal.
Sample Answer
There are a few steps to follow in order to implement a Binary Search Tree.
Example: Binary Search Trees are used in database indexing, like finding a record in a database table quickly by organizing keys hierarchically.
Why Do Interviewers Ask This Question?
They’re verifying your ability to analyze algorithmic complexity. Showing how O(n), O(log n), or O(n²) relate to performance constraints is crucial for real-world optimizations.
Sample Answer
Big O notation measures how an algorithm's runtime or memory usage grows with input size. It helps you understand the efficiency of your code, ensuring it can handle large datasets without performance issues.
Example: If you’re searching for a value in a list:
Why Do Interviewers Ask This Question?
It’s one of those coding questions for placement that’s asked to reveal your understanding of graph or tree traversal. BFS is often used for shortest paths in unweighted graphs, while DFS suits exhaustive searches or detecting cycles.
Sample Answer
Breadth-First Search (BFS) explores all neighbors at the current depth before moving deeper. It’s ideal for finding the shortest path in unweighted graphs.
Depth-First Search (DFS), on the other hand, explores as far as possible along one branch before backtracking. It’s better for tasks like detecting cycles or exploring all possible paths.
Example:
Why Do Interviewers Ask This Question?
They want to see your approach to reducing time or space complexity — like using memoization, more efficient data structures, or parallelizing tasks when possible.
Sample Answer
Algorithm optimization involves identifying inefficiencies and improving runtime or memory usage.
Here are the key strategies:
Example: Optimizing a recommendation system by caching user preferences reduces repeated database queries, improving response time significantly.
Why Do Interviewers Ask This Question?
It’s one of those coding interview questions that’s asked to test your frequency-counting or indexing strategies. Efficient solutions often involve a single pass (or two passes) with extra space or a hashing approach.
Sample Answer
To find the first non-repeated character in a string, traverse the string and count the occurrences of each character. Return the first character that has a count of 1.
Example: For the string "swiss", the first non-repeated character is 'w'.
Why Do Interviewers Ask This Question?
This question checks if you can manually parse, store, and rebuild strings. It also tests your attention to edge cases like trailing spaces or punctuation.
Sample Answer
There are simple steps to reverse the words in a sentence:
Code Snippet and Explanation:
This code splits the sentence into words manually, stores them in a list, and then rearranges the words in reverse order to form a new sentence. It avoids using built-in functions like split() or join().
def reverse_words(sentence):
words = []
word = ""
for char in sentence: # Split words manually
if char == " ":
words.append(word)
word = ""
else:
word += char
words.append(word) # Add the last word
reversed_sentence = ""
for i in range(len(words) - 1, -1, -1): # Reverse the order of words
reversed_sentence += words[i] + " "
return reversed_sentence.strip()
# Input
sentence = "hello world"
# Output
print(reverse_words(sentence)) # Output: "world hello"
Input:
sentence = "hello world"
Output:
"world hello"
Why Do Interviewers Ask This Question?
It’s one of those coding questions that assesses your ability to think of string concatenation solutions (s1 + s1 contains s2 if they’re rotations). Also, it reveals if you consider edge cases (equal length, empty strings).
Sample Answer
Two strings are rotations of each other if one string can be obtained by rotating the other. To check, concatenate one string to itself and see if the other string is a substring.
Example: For "abcd" and "dabc", concatenating "abcd" with itself ("abcdabcd") contains "dabc", so they are rotations.
Why Do Interviewers Ask This Question?
Permutations test recursion and backtracking. Interviewers want to see if you can handle repeated characters and understand the exponential complexity that comes with generating all permutations.
Sample Answer
To find all permutations of a string, use recursion:
Example: For "abc", the permutations are ["abc", "acb", "bac", "bca", "cab", "cba"].
Why Do Interviewers Ask This Question?
It’s asked to check if you can write robust, fault-tolerant code. Properly catching, logging, and re-throwing exceptions is critical in production-grade applications.
Sample Answer
Exception handling ensures that errors are caught and managed appropriately.
Why Do Interviewers Ask This Question?
This tests creative use of core data structures. They look for whether you can maintain FIFO behavior (queue) with LIFO operations (stack), and manage time complexities well.
Sample Answer
To implement a queue using two stacks, here’s what you need to do:
Why Do Interviewers Ask This Question?
This is one of those coding questions that’s asked to see your recursive (or level-order) approach and handling of null checks. Depth calculation is a fundamental task in tree manipulation.
Sample Answer
Absolutely.
Here’s a code that calculates the maximum depth of a binary tree by recursively checking the depth of the left and right subtrees.
class TreeNode:
def __init__(self, value=0, left=None, right=None):
self.value = value
self.left = left
self.right = right
def max_depth(root):
if not root:
return 0
left_depth = max_depth(root.left)
right_depth = max_depth(root.right)
return max(left_depth, right_depth) + 1
# Example Usage
root = TreeNode(1)
root.left = TreeNode(2)
root.right = TreeNode(3)
root.left.left = TreeNode(4)
root.left.right = TreeNode(5)
print(max_depth(root)) # Output: 3
Input:
1
/ \
2 3
/ \
4 5
Output:
3 # The maximum depth of the tree is 3
Also Read: 5 Types of Binary Tree Explained [With Illustrations]
Why Do Interviewers Ask This Question?
They need to ensure you’re comfortable breaking down problems and identifying base/recursive cases. Examples like factorials or file-system traversal underscore real-world recursion usage.
Sample Answer
Recursion involves a function calling itself to solve smaller instances of a problem until a base condition is met.
Here are some practical examples of doing so.
Why Do Interviewers Ask This Question?
These are staple O(n log n) algorithms. Explaining them well shows you can handle divide-and-conquer patterns, pivot strategies, and merges in production contexts.
Sample Answer
Modern sorting algorithms like Merge Sort and Quick Sort are frequently used for their O(nlogn)O(n \log n)O(nlogn) performance in average cases.
Merge Sort Code Snippet and Explanation:
This Merge Sort code divides the array into two halves, recursively sorts each half, and then merges the sorted halves into a single sorted array. This process continues until the entire array is sorted.
def merge_sort(arr):
if len(arr) <= 1:
return arr
mid = len(arr) // 2
left = merge_sort(arr[:mid])
right = merge_sort(arr[mid:])
return merge(left, right)
def merge(left, right):
result = []
while left and right:
if left[0] < right[0]:
result.append(left.pop(0))
else:
result.append(right.pop(0))
result.extend(left or right)
return result
# Input
arr = [8, 4, 2, 6, 5]
# Output
print(merge_sort(arr)) # Output: [2, 4, 5, 6, 8]
Input:
arr = [8, 4, 2, 6, 5]
Output:
[2, 4, 5, 6, 8]
Quick Sort Code Snippet and Explanation:
This Quick Sort code chooses a pivot (the first element), divides the array into smaller (less) and larger (greater) values, and recursively sorts them. The final result is a merged, sorted array.
def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
less = [x for x in arr[1:] if x <= pivot]
greater = [x for x in arr[1:] if x > pivot]
return quick_sort(less) + [pivot] + quick_sort(greater)
# Input
arr = [10, 3, 7, 1, 9]
# Output
print(quick_sort(arr)) # Output: [1, 3, 7, 9, 10]
Input:
arr = [10, 3, 7, 1, 9]
Output:
[1, 3, 7, 9, 10]
Why Do Interviewers Ask This Question?
It’s one of those coding questions for placement that’s asked to see if you can optimize by caching overlapping subproblems (memoization) or bottom-up tabulation. DP is crucial in complex optimization or combinatorial problems.
Sample Answer
DP involves solving problems by storing the results of subproblems for reuse.
Example: Calculating the Fibonacci sequence using memoization
This code calculates Fibonacci numbers using dynamic programming by storing already-computed values (memoization). This avoids repeated calculations, making it much faster than plain recursion. For n = 6, it returns 8.
def fibonacci(n, memo={}):
if n in memo:
return memo[n]
if n <= 2:
return 1
memo[n] = fibonacci(n - 1, memo) + fibonacci(n - 2, memo)
return memo[n]
# Input
n = 6
# Output
print(fibonacci(n)) # Output: 8
Input:
n = 6 # Find the 6th Fibonacci number
Output:
8 # The 6th Fibonacci number is 8
Why Do Interviewers Ask This Question?
They’re trying to check if you can connect O-notation to practical scenarios — like O(n) for scanning an array or O(log n) for binary search — rather than just reciting definitions.
Sample Answer
Time complexity measures the growth of an algorithm's runtime as input size increases.
Here’s an explanation through real examples.
1. O(1)O(1)O(1) - Constant Time: The runtime is independent of input size.
Real Example: Accessing an element in an array by its index.
arr = [10, 20, 30, 40]
print(arr[2]) # Output: 30
Explanation: No matter how large the array is, accessing an element by index takes the same amount of time.
2. O(n)O(n)O(n) - Linear Time: The runtime grows linearly with the input size.
Real Example: Finding the maximum value in an unsorted list.
arr = [3, 5, 1, 7, 9]
print(max(arr)) # Output: 9
Explanation: The algorithm must iterate through every element to find the maximum value, so the runtime scales with the number of elements.
Why Do Interviewers Ask This Question?
This is one of those coding questions that tests paradigm knowledge—procedural focuses on step-by-step logic, while functional emphasizes immutability and pure functions. Employers value flexibility in different styles.
Sample Answer
Procedural Programming follows a sequence of steps or instructions. Functional Programming, on the other hand, focuses on what to achieve by using pure functions and avoiding state or mutable data.
Here are the differences between the two.
Feature |
Procedural Programming |
Functional Programming |
State | Uses and modifies program state (mutable variables). | Avoids modifying state; relies on immutability. |
Code Reusability | Encourages reusability, but often tied to the program's state. | High reusability due to stateless functions. |
Use of Loops | Relies on loops for iteration (e.g., for, while). | Uses recursion or functional constructs (e.g., map, filter, reduce). |
Example Languages | C, Python (procedural style), Java. | Haskell, Lisp, Python (functional style). |
Real-world Examples | Writing step-by-step instructions for task automation. | Data transformation pipelines or mathematical computations. |
Why Do Interviewers Ask This Question?
They want insight into handling unstructured or semi-structured data at scale. It also clarifies if you understand trade-offs with consistency, schemas, and distribution models.
Sample Answer
SQL databases use structured data and predefined schemas, ideal for relational data and complex queries (e.g., MySQL). On the contrary, NoSQL databases handle unstructured or semi-structured data, providing scalability and flexibility (e.g., MongoDB).
Example:
Why Do Interviewers Ask This Question?
They look for strong coding standards—like meaningful naming, modular design, and documentation. It indicates how well you collaborate and minimize tech debt.
Sample Answer
To ensure code readability and maintainability, here’s what needs to be followed:
Why Do Interviewers Ask This Question?
It’s one of those coding interview questions that’s asked to gauge your communication skills and ability to simplify jargon. Tech leads or senior devs often must align stakeholders who lack deep technical expertise.
Sample Answer
"Here’s how I would explain an API to a non-technical person: An API is like a waiter in a restaurant. You (the app) request an item from the menu (data), and the waiter (API) fetches it from the kitchen (server) and delivers it back to you."
Why Do Interviewers Ask This Question?
Employers want to see a methodical strategy: replicating the bug, logging/monitoring, isolating root causes. It’s a window into your systematic vs. guess-based mindset.
Sample Answer
To debug a difficult issue, here’s what needs to be done:
Why Do Interviewers Ask This Question?
They’re verifying collaboration skills, safe code rollbacks, and structured workflows (branching, merging). Mastery of Git is nearly mandatory for modern development teams.
Sample Answer
Version control systems like Git are essential for tracking changes, enabling collaboration, and maintaining code history. They allow developers to work on the same project without overwriting each other’s work and provide a safety net to revert to previous versions if needed.
These coding interview questions are designed for senior engineers, tech leads, or professionals with extensive hands-on experience who are ready to tackle architectural decisions, intricate design patterns, and cross-functional problem-solving.
If your role involves mentoring junior developers, aligning technical solutions with business goals, and ensuring robust system performance at scale, these are the types of questions you’ll have to tackle for cracking the coding interview.
Here are some skills you’ll hone by practicing the coding questions in this section:
Now, let’s explore the most crucial 6 coding interview questions with solutions for experienced professionals.
Why Do Interviewers Ask This Question?
This is one of those coding questions for placements that’s asked to see if you can architect complex systems that remain maintainable and scalable over time. SOLID principles also reveal whether you can create code that’s easy for teams to extend without introducing new bugs.
Sample Answer
The SOLID principles are a set of five guidelines for object-oriented programming:
Example: Applying SRP, a class Invoice should only handle invoice details, while a separate class InvoicePrinter should handle printing invoices.
Why Do Interviewers Ask This Question?
They are testing your awareness of emerging trends and your adaptability to new technologies. It also shows if you’re proactive about future-proofing your skill set to match evolving industry needs.
Sample Answer
In 2025, you should be proficient in these key programming languages:
Why Do Interviewers Ask This Question?
Interviewers want insights into your problem-solving approach, leadership qualities, and ability to handle setbacks. Hearing about real challenges and the strategies you used demonstrates resilience and resourcefulness at a senior level.
Sample Answer
"In my last project, I worked on developing an e-commerce site, where the biggest challenge was optimizing database queries to handle high traffic. By analyzing query performance, implementing indexing, and caching frequent queries, we improved response time by 50%."
Why Do Interviewers Ask This Question?
It’s one of those high-level coding questions that’s asked to check for a growth mindset and ongoing commitment to professional development. Experienced hires are expected to consistently learn new tools, frameworks, and best practices to remain effective in fast-paced tech environments.
Sample Answer:
"I regularly take courses on platforms like upGrad and follow tech blogs like Medium and Dev.to. I recently completed a course on cloud architecture to improve my understanding of AWS and Azure services."
Why Do Interviewers Ask This Question?
Rapid adoption of unfamiliar tech is common in senior roles. Employers want to know how you approach learning curves, locate resources, and integrate new knowledge into a product or system under tight timelines.
Sample Answer
"In a recent project, I was tasked with implementing CI/CD pipelines using Jenkins, which I hadn’t used before. I dedicated a week to studying its documentation and watching tutorials, then successfully set up the pipeline, reducing deployment time by 30%."
Why Do Interviewers Ask This Question?
They’re gauging your ability to craft reusable, elegant solutions to recurring problems. Proficiency with patterns like Factory, Observer, or Singleton indicates that you can make architectural decisions that reduce complexity in large codebases.
Sample Answer
Design patterns are best practices for solving recurring problems in software design.
Examples:
From the rise of remote interviewing to an increased emphasis on design and soft skills, staying aware of the latest trends can make the difference between a successful interview and a missed opportunity.
Today’s coding interviews are more holistic and challenging but also more transparent, giving well-prepared candidates a clear chance to shine in front of startups and tech giants alike.
Here are the several key trends that stand out in coding interviews in 2025:
Preparing for coding interviews requires a structured approach to mastering technical concepts, practicing problem-solving, and building confidence. This section provides actionable strategies to help you excel in your next interview.
Enhancing Communication Skills: Practice explaining your solutions clearly and step-by-step, as interviewers value both coding and communication skills.
Preparing for coding interviews is just the beginning. To truly stand out and accelerate your career, you need comprehensive learning that combines technical skills with hands-on experience.
upGrad’s programming courses offer exactly that — an opportunity to master coding, build real-world projects, and stay ahead in the competitive tech industry.
Here are some of our software development courses that will take you a long way:
You can explore more software development courses by upGrad.
Mastering coding questions for interviews is crucial for securing a tech job in 2025. By practicing these 65 coding interview questions and answers, you can build problem-solving skills, improve coding efficiency, and confidently tackle technical assessments.
Stay consistent, focus on optimizing solutions, and keep refining your knowledge to excel in your next interview. Still worried about which option will future-proof your career? Fret not, upGrad can help – book a free career counseling session with our experts and get the answers – and peace of mind – you deserve. You can also visit your nearest offline upGrad Center.
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Stay informed with our widely-read Software Development articles, covering everything from coding techniques to the latest advancements in software engineering.
Reference Links:
https://www.statista.com/statistics/1296727/programming-languages-demanded-by-recruiters/
https://interviewing.io/blog/when-is-hiring-coming-back-predictions-for-2024
https://algodaily.com/blog/coding-interview-trends-in-2024
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