Cracking the Coding Interview with 65 Coding Questions in 2025

Did you know? Coders skilled in Python, JavaScript, and Java are in high demand, with nearly 40% of recruiters actively seeking talent in these areas. Preparing for coding interviews can be a pivotal step in advancing your career.

Coding interviews are more than just problem-solving. They test your grasp of algorithms, data structures, time-space optimization, and the ability to write clean, efficient code under pressure. Whether you're a recent graduate preparing for your first tech role or an experienced developer targeting top-tier companies, this guide equips you with the tools to succeed.

In this blog, you'll explore 65 carefully selected coding questions that reflect real-world interview scenarios. From arrays, linked lists, and dynamic programming to system design fundamentals, each question comes with a clear, well-explained solution. 

Want to enhance your coding skills? upGrad’s Online Software Development Courses provide you with the latest tools and strategies to stay ahead in the ever-evolving tech world. Enroll today and build the future of web development!

Boost your tech career with expert-led AI and Machine Learning Courses and practical online data science courses designed to help you ace coding interviews.

Top 26 Basic Coding Questions for Interviews

This section's basic coding questions are perfect for you if you're a beginner or an entry-level candidate, whether you're a recent graduate, a coding bootcamp alum, or someone looking to build a strong foundation in programming.

If you’re new to programming interviews or returning to the field after a long gap, these questions will help you crack the coding interview by building a solid foundation of skills and confidence.

By working through these 26 essential coding questions for placement, you’ll hone the following skills:

• Problem Solving: Breaking down coding challenges into smaller, manageable tasks.
• Syntax Mastery: Gaining comfort with language-specific constructs, loops, and conditionals.
• Foundational Data Structures: Understanding arrays, linked lists, and how to manipulate them.
• Basic Algorithmic Thinking: Learning simple search, sort, and recursion techniques.
• Code Readability & Organization: Emphasizing consistent, maintainable coding practices even at a beginner’s level. 

Build job-ready coding skills and boost your career with these advanced, hands-on programs upGrad’s  :

• Advanced Certificate Program in Generative AI
• Professional Certificate Program in Data Science and AI
• Full-stack Development Bootcamp

Now, let’s explore all 26 coding interview questions with solutions in detail.

1. What is a Data Structure?

How to Answer:
Interviewers ask this to check if you understand how data is organized and accessed. Keep it simple. Define it, mention why it's useful, and give a quick example.

• Give a clear definition.
• Mention why it's important.
• Use a quick, relatable example.

Sample Answer:
A data structure organizes and stores data so it can be used efficiently. It’s essential for writing optimized code and solving problems effectively.

For example, arrays let you access elements instantly by index, while stacks and queues help manage order in processing tasks.

Struggling with coding interviews or core CS concepts? upGrad’s Free Data Structures & Algorithms Course offers 50 hours of expert-led learning to help you master arrays, trees, sorting, recursion, and more. Learn at your own pace, apply concepts to real-world problems, and earn a free certificate to showcase your skills.

How to Answer:
Interviewers ask this to see if you understand how data is stored in contiguous memory and how indexing works. Keep it straightforward. Define what an array is, explain why it’s useful, and give a simple example.

• Give a clear definition.
• Mention why it matters.
• Use a quick, relatable example.

Sample Answer:
An array is a collection of elements of the same type stored in contiguous memory locations. It allows you to access any aspect quickly using its index. Arrays are useful for organizing data that has a fixed size and needs fast, direct access.

For example, an array can hold all the temperature readings for a week if you're building an app that records daily temperatures. This makes it easy to retrieve or update any day’s temperature quickly.

Also Read: What is an Array in Data Structures? Key Concepts, Types, and Operations

3. What is a Linked List and its Applications?

How to Answer:
Interviewers ask this to see if you understand dynamic data structures and can explain their basic components and uses. Keep it straightforward. Define what a linked list is, mention its key parts, and give a simple example. Then briefly touch on practical applications.

• Give a clear definition.
• Explain the structure simply.
• Provide a relatable example.
• Mention common applications.

Sample Answer:
A linked list is a dynamic data structure made up of nodes. Each node contains data and a pointer to the next node. This setup allows easy insertion and deletion without reorganizing the whole structure.

For example, imagine a to-do list app where tasks are frequently added or removed. A linked list lets you update the list efficiently without shifting all items like you would in an array.

Typical uses of linked lists include task scheduling, where tasks can be reordered dynamically, and undo/redo features in text editors, which track changes over time.

Also Read: What is Linear Data Structure and its Types? Explore Differences With Nonlinear Structures

4. Can You Explain the Difference Between an Array and a Linked List?

How to Answer:
Interviewers ask this to determine whether you understand the core differences between arrays and linked lists. Keep your explanation clear and practical. Define both, highlight key differences, and use a simple example.

• Give a clear definition.
• Mention how they differ in structure and usage.
• Use a quick, relatable example.

Sample Answer:
An array stores elements in a fixed-size, contiguous memory block, allowing instant access using an index. A linked list, on the other hand, is made up of nodes, each pointing to the next, which makes it flexible in size but slower for access.

Here’s how you compare:

Example:
If you're tracking students in a class:
Use an array if the number of students is fixed.
Use a linked list if the class size changes often.

Code Snippet and Explanation:

# Array Example
students_array = ["Sneha", "Dinesh", "Arup"]
print("Array: Second student:", students_array[1])
students_array.append("Pooja")
print("Array: Updated list:", students_array)

# Linked List Example
class Node:
    def __init__(self, student):
        self.student = student
        self.next = None

student1 = Node("Sneha")
student2 = Node("Dinesh")
student3 = Node("Arup")
student1.next = student2
student2.next = student3

new_student = Node("Pooja")
student3.next = new_student

print("Linked List: Student List:")
current_student = student1
while current_student:
    print(current_student.student)
    current_student = current_student.next

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

5. Can You Explain the Concept of Object-Oriented Programming (OOP)?

How to Answer:
Interviewers ask this to see if you understand programming beyond just writing functions. Keep it simple. Define OOP, explain why it matters, and use a quick example to show how it works.

• Give a clear definition.
• Mention why it's useful.
• Use a simple, relatable example.

Sample Answer:
Object-Oriented Programming (OOP) is a way of organizing code using objects—instances of classes that bundle data and behavior together. It helps make code modular, reusable, and easier to maintain.

For example, in an online store, you might have a Product class with attributes like name and price. A Laptop or Phone would be object based on that class. You can also have a Clothing class that inherits from Product and adds things like size or color.

Want to learn the framework of classes and objects and explore OOP principles: Abstraction, Encapsulation, Inheritance, and Polymorphism? Enroll in upGrad’s free certificate course, Java Object-Oriented Programming.

Also Read: What are the Advantages of Object-Oriented Programming?

6. What are Classes and Objects in OOP?

How to Answer:
Interviewers ask this to see if you understand how object-oriented programming models real-world entities. Keep it simple. Define both terms, explain their relationship, and use a quick example.

• Give a clear definition of classes and objects.
• Explain how they relate.
• Use a short, real-world example.

Sample Answer:
A class is a blueprint for creating objects. It defines attributes and behaviors. An object is an instance of a class with actual values.

For example, a class might define properties like name and age in a student system. Each student you create is an object with their own name and age.

7. Understanding Inheritance in OOP with Examples: Can You Explain? 

How to Answer:
Interviewers ask this to see if you understand object-oriented programming (OOP) and can apply inheritance to write clean, reusable code. Keep your explanation simple: define it, explain its benefits, and give a short example.

• Give a clear definition.
• Explain why it’s useful in real-world coding.
• Share a quick, relatable code example.

Sample Answer:
Inheritance lets a class (called a child or subclass) reuse code from another class (the parent or superclass). It helps reduce duplication and keeps your code organized.

For example, a Vehicle class might define common properties like brand and model, along with a method like start_engine(). Child classes like Car and Motorcycle can inherit from Vehicle, reuse its code, and add their own specific features.

Example:

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!"

class Car(Vehicle):
    def __init__(self, brand, model, airbags):
        super().__init__(brand, model)
        self.airbags = airbags

    def start_engine(self):
        return f"{self.brand} {self.model}: Engine started with advanced features!"

    def safety_features(self):
        return f"{self.brand} {self.model} has {self.airbags} airbags."

class Motorcycle(Vehicle):
    def __init__(self, brand, model, handlebars):
        super().__init__(brand, model)
        self.handlebars = handlebars
andlebars

Input:
A Car with brand "Toyota", model "Camry", and 6 airbags.
A Motorcycle with brand "Harley-Davidson", model "Street 750", and "Cruiser handlebars".

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 

8. What is Polymorphism in OOP, and Why is it Important?

How to Answer:
Interviewers ask this to see if you understand how one method or interface can behave differently depending on the context. Keep it clear and relatable.

• Give a clear definition.
• Mention why it’s useful.
• Use a quick, everyday example.

Sample Answer:
Polymorphism in OOP means a method can behave differently depending on the object being used. It helps write cleaner, more adaptable code.

For example, a "start" command on different devices:
On a car, it turns on the engine.
On a laptop, it boots up the system.
The command is the same, but the action depends on the device using it

Also Read: Types of Polymorphism in Java [Static & Dynamic Polymorphism with Examples].

9. What is the Difference Between Public, Private, and Static Classes?

How to Answer:
Interviewers ask this to test your understanding of access control and class behavior in object-oriented programming. Keep it simple. Define each, explain its use, and use a quick, clear analogy.

• Give a short definition of each type.
• Explain when and why they're used.
• Use a relatable example or analogy.

Sample Answer:
A public class can be accessed from anywhere in the project. A private class is restricted to the containing class or file. A static class can’t be instantiated and usually holds utility methods.

Think of it like this:
A public class is like a public library—open to everyone.
A private class is like your notebook—only you can use it.
A static class is like a toolbox—you don’t create it, you just use its tools directly.

10. What is a Loop in Programming and its Types?

How to Answer:
Interviewers ask if you understand how to automate tasks and choose the right loop for the situation. Keep it simple. Define it, explain its use, and touch on the main types.

• Give a clear definition.
• Mention why it's useful.
• Briefly highlight the types with quick examples.

Sample Answer:
A loop lets you repeat code based on a condition, which helps automate repetitive tasks efficiently.

There are three main types:

• For loop – runs several times, like looping through a list.
• While loop – runs as long as a condition is proper, like reading a file until it ends.
• Do-while loop – runs at least once before checking the condition, like prompting a user for input.

11. Can You Explain Conditional Statements with Examples?

How to Answer:
Interviewers ask if you understand how to automate tasks and choose the right loop for the situation. Keep it simple. Define it, explain its use, and touch on the main types.

• Give a clear definition.
• Mention why it's useful.
• Briefly highlight the types with quick examples.

Sample Answer:
A loop lets you repeat code based on a condition, which helps automate repetitive tasks efficiently.

There are three main types:

• For loop – runs several times, like looping through a list.
• While loop – runs as long as a condition is proper, like reading a file until it ends.
• Do-while loop – runs at least once before checking the condition, like prompting a user for input.

seats_available = 5
if seats_available > 0:
   print("Seats are available!")
if-else Statement Example:
Used when there are two possible outcomes.
python
CopyEdit
seats_available = 0
if seats_available > 0:
   print("Seats are available!")
else:
   print("Sorry, no seats are available.")

if-elif-else Statement Example:
Used for 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

12. How Do You Implement a For Loop and While Loop?

How to Answer:
Interviewers ask this to check if you can use loops to handle repetitive tasks. Keep it simple. Explain when to use each loop and give a quick, clear example.

• Say when to use a for loop.
• Say when to use a while loop.
• Use short, real-world examples.

Sample Answer:
A for loop is used when you know how many times you want to repeat something, like going through a list. A while loop runs as long as a condition is true.

For example, use a for loop to sum all numbers in a list.
Use a while loop to keep asking for a password until the correct one is entered.

13. What is Recursion, and How Does it Work?

How to Answer:
Interviewers ask this to see if you understand how recursion works under the hood especially base cases, stack memory, and when recursion is a better fit than iteration. Keep your explanation simple and focused.

• Give a clear definition.
• Mention how it works.
• Use a simple, relatable example.

Sample Answer:
Recursion is when a function calls itself to solve smaller parts of a problem. It stops when it reaches a base case.

For example, to count down from 5, a recursive function calls itself with 4, then 3, and so on, until it hits 1 and stops.

Also Read: Recursion in Data Structure: How Does it Work, Types & When Used

14. How Do You Reverse a String in Your Preferred Language?

How to Answer:
Interviewers ask this to test your basic string manipulation skills. Keep it simple. Explain the approach, mention built-in methods if applicable, and give a short code example.

Explain the logic or method you’d use
Give a clear definition.

• Explain why it matters in coding.
• Use an easy-to-follow example.

Sample Answer:
Big O notation describes how an algorithm's performance changes as the input size grows. It's important to write efficient code that can scale.

For example, a linear search goes through each item one by one — that's O(n). A binary search splits the list in half each time — that's O(log n), which is much faster for large, sorted lists.

Also Read: Algorithm Complexity and Data Structure: Types of Time Complexity

15. How to Determine if a String is a Palindrome?

How to Answer:
Interviewers ask this to assess your understanding of graph or tree traversal techniques. Stick to the core differences. Define each, explain when they’re useful, and give quick, relatable examples.

• Give a clear definition.
• Mention when each is typically used.
• Use simple, real-world examples.

Sample Answer:
 Breadth-First Search (BFS) explores all neighboring nodes level by level. It’s great for finding the shortest path in unweighted graphs.
 Depth-First Search (DFS) goes deep down one path before backtracking. It’s useful for detecting cycles or exploring all possible paths.

Example:
BFS is like finding the shortest route in a city map.
DFS is like solving a maze by following one path to the end before trying another.

Also Read: Difference Between DFS and BFS: DFS vs BFS, Similarities, and More

16. Can You Calculate the Number of Vowels and Consonants in a String?

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

17. How to Find the Maximum Element in an Array?

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

18. How to Sort an Array of Integers in Ascending Order?

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]

19. How to Find Anagrams of a Given String?

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:

1. Sort both strings: "listen" → "eilnst", "silent" → "eilnst".
2. Compare the sorted results: If they’re the same, the words are anagrams.

Also Read: Anagram Program in Python | Methods and Examples

20. What are the Methods to Remove Duplicates from an Array?

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.

1. Use a set: A set automatically eliminates duplicates, but it may lose the order of elements.
2. Use a loop: Iterate through the array, adding elements to a new list only if they haven’t already been added.
3. Use In-Place Modification (Memory-Efficient): Modify the array in place to remove duplicates without using extra space. This works for sorted arrays.

21. How to Find the Second Largest Number in an Array?

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:

• Traverse the array to find the largest number.
• Traverse again to find the largest number smaller than the first.

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

22. How to Reverse an Array Without Using Additional Data Structures?
Why Do Interviewers Ask This Question?

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.

23. How Do You Check if a Number is Prime?

Why Do Interviewers Ask This Question?

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). 

• If it’s divisible, it’s not prime; otherwise, it is prime. 
• For 7, no divisors are found, so it’s prime.

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

24. Can You Calculate the Factorial of an Integer Using Iteration and Recursion?

Why Do Interviewers Ask This Question?

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. 

• Each iteration updates the result until it computes the factorial of n. 
• For 5, it multiplies 1 × 2 × 3 × 4 × 5 to get 120.

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). 

• It stops when it reaches 1 and then calculates the final result by multiplying all the returned values. 
• For 5, the result is 120.

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

25. What is LIFO, and How is it Implemented Using a Stack?

Why Do Interviewers Ask This Question?

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:

• Push: Add an element to the top of the stack.
• Pop: Remove the top element.
• Peek: View the top element without removing it.

26. What is Your Understanding of FIFO and Queue Implementations?

Why Do Interviewers Ask This Question?

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.

1. Arrays: Fixed-size with front and rear pointers.
2. Linked Lists: Dynamic with pointers updating during insertions and deletions.

Example:

• FIFO Queues: Process tasks in the order they are added, ideal for task scheduling.
• Priority Queues: Dequeue elements based on priority, not arrival time, used in algorithms like Dijkstra’s shortest path.

Top 33 Advanced-level Coding Interview Questions 

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:

• Algorithm Optimization: Crafting and refining solutions for efficiency and scalability.
• Complex Data Structure Mastery: Working with trees, graphs, heaps, and more advanced custom structures.
• Performance Considerations: Balancing time complexity and memory constraints for real-world use cases.
• Problem Decomposition: Breaking down intricate tasks and applying strategic debugging or design patterns.

Now, let’s explore the most asked coding interview questions with solutions to help you in cracking the coding interview.

1. Can You Explain Binary Trees and Their Uses?

How to Answer:

Interviewers ask this to check your understanding of hierarchical data structures and how they’re used in organizing or retrieving information. Keep it simple. Define it, explain why it matters, and give a practical example.

• Give a clear definition.
• Mention why it’s essential.
• Use a quick, relatable example.

Sample Answer:

A binary tree is a structure where each node has up to two children: left and right. It helps organize data for fast searching and sorting.

For example, binary search trees let you quickly find or insert values, and are used in things like file systems or auto-complete features.

Also Read: 5 Types of Binary Trees: Key Concepts, Structures, and Real-World Applications in 2025

2. What are Binary Search Trees, and How Do They Work?

How to Answer:
Interviewers ask this to see if you understand how ordered data structures work and how they enable fast operations. Keep it focused. Define it, explain why it matters, and use a relatable example.

• Give a clear definition.
• Mention why it's important.
• Use a quick, real-world example.

Sample Answer:
A Binary Search Tree (BST) is a tree structure where each node has at most two children. The left child holds values less than the parent, and the right holds values greater.

It's useful for fast lookups, insertions, and deletions — all in O(log n) time on average.

Think of managing a sorted contact list. Instead of scanning the whole list, a BST helps you quickly narrow down where a name might be, making searches much faster.

Also Read: Binary Tree vs Binary Search Tree: Difference Between Binary Tree and Binary Search Tree

3. What is the Difference Between Linear and Non-Linear Data Structures?

How to Answer:
Interviewers ask this to check if you can distinguish between types of data structures and choose the right one based on the problem. Keep it brief. Define each, highlight the key difference, and give a simple example.

• Define both types clearly.
• Mention the main difference.
• Use relatable examples.

Sample Answer:
Linear data structures store elements in a sequence, like arrays or linked lists. Non-linear data structures organize data hierarchically, like trees or graphs.

For example, arrays let you access items by index in order, while a tree shows parent-child relationships, such as a folder system on your computer.

Also Read: Difference Between Linear and Non-Linear Data Structures

4. How Would You Implement the Bubble Sort Algorithm?

How to Answer:
Interviewers ask this to test your understanding of sorting basics, how you use loops, and your ability to explain logic clearly. Focus on the concept and why it matters, then walk through a simple example.

• Explain the concept in simple terms.
• Mention where it’s useful.
• Walk through a basic example with clarity.

Sample Answer:
 Bubble sort is a simple sorting algorithm that repeatedly compares and swaps adjacent elements if they’re in the wrong order. With each pass, the largest unsorted element moves to its correct position. It’s a good example of using nested loops and understanding time complexity basics.

5. Can You Explain How Insertion Sort Works with an Example?

How to Answer:
Interviewers ask this to see if you understand the logic behind basic sorting and how nested loops work. Keep it clear and relatable. Define the concept, explain why it's used, and give a quick, real-world example.

• Give a simple definition.
• Mention when it’s useful.
• Use a quick analogy or example.

Sample Answer:
Insertion sort goes through elements one by one and places each into its correct position in the already sorted part of the list. It’s great for small or nearly sorted datasets.
Think of sorting playing cards in your hand, you pick one at a time and insert it where it fits among the cards you’ve already arranged.

Also Read: Sorting in Data Structure: Categories & Types [With Examples]

6. How Do You Implement Binary Search in a Sorted Array?

How to Answer:
Interviewers ask this to check your understanding of divide-and-conquer logic. Keep it simple. Define what binary search does, highlight its efficiency, and give a quick, clear example.

• Give a concise explanation.
• Mention why it’s efficient.
• Use an easy-to-understand analogy.

Sample Answer:
Binary search finds an element by repeatedly dividing the sorted array in half. It compares the target with the middle value to decide which half to search next.
It’s efficient because it cuts the search space in half each time, giving a time complexity of O(log n).

7. What is the Best Sorting Algorithm and Why?

How to Answer:
Interviewers ask if you understand how sorting algorithms perform in different situations. Keep it focused. Compare key algorithms, highlight their strengths, and explain when you’d use each.

• Acknowledge there's no one-size-fits-all answer.
• Mention the most commonly used algorithms.
• Briefly explain their strengths and trade-offs.

Sample Answer:
There’s no single best sorting algorithm—it depends on the context.
Merge Sort is stable and great for large datasets but needs extra memory. Quick Sort is usually faster but can slow down with bad pivot choices. For almost-sorted data, Insertion Sort is simple and efficient. It’s all about choosing the right tool for the problem.

8. Can You Print a Fibonacci Sequence Using Recursion?

How to Answer:
Interviewers ask this to test your understanding of recursion and how well you can break a problem into smaller subproblems. Keep your explanation focused. Define the sequence, explain recursion briefly, and walk through a simple example.

• Give a clear definition.
• Mention how recursion works in this context.
• Use a short, step-by-step example.

Sample Answer:
The Fibonacci sequence is a series where each number is the sum of the two before it. Recursion works by having the function call itself with smaller inputs until it reaches a base case.

Example: To print the first 5 numbers:
Start with 0 and 1 → 0 + 1 = 1 → 1 + 1 = 2 → 1 + 2 = 3.

Code Snippet and Explanation:
This function returns n if it's 0 or 1 (base case). Otherwise, it adds the two previous Fibonacci values by calling itself.

def fibonacci(n):
   if n <= 1:
       return n
   return fibonacci(n - 1) + fibonacci(n - 2)
# Print first 5 Fibonacci numbers
for i in range(5):
   print(fibonacci(i), end=" ")  # Output: 0 1 1 2 3

Input:

 n = 5

Output:

 0 1 1 2 3

9. Can You Find the Length of the Longest Substring Without Repeating Characters?

How to Answer:
Interviewers ask this to see if you understand string manipulation and how to apply efficient techniques like sliding windows. Stay clear and focused.

• Explain the approach briefly.
• Mention why it's efficient.
• Use a quick example.

Sample Answer:
Use a sliding window to track characters and their positions. If a character repeats, move the start of the window forward. Update the max length as you go.

For example, in "abcabcbb", the longest substring without repeating characters is "abc", so the answer is 3.

Also Read: Sliding Window Technique: Everything You Need to Know

10. Can You Explain the Concepts of Hashmaps and Their Applications?

How to Answer:
Interviewers ask this to see if you understand how hashing enables quick lookups and efficient data handling. Keep it direct. Define it, explain why it’s useful, and give a simple example.

• Give a clear definition.
• Mention why it's important.
• Use a quick, relatable example.

Sample Answer:
A hashmap stores key-value pairs and lets you access data quickly using keys. It uses a hash function to map each key to an index, making lookups fast—usually O(1) on average.

It’s useful for tracking counts, avoiding duplicates, or caching.
For example, you can use a hashmap to count how many times each word appears in a sentence.

Also Read: Hashmap Interview Questions & Answers [For Beginners & Experienced]

Example: Hashmaps are used in programming dictionaries, where you store words (keys) and their definitions (values). 

11. What is a Graph, and How is it Used in Programming?

How to Answer:
Interviewers ask this to see if you're comfortable with relationships between data points and understand key graph concepts like adjacency, traversal, and real-world applications. Keep it simple—define it, explain why it matters, and give a quick example.

• Give a clear definition.
• Mention why it’s essential.
• Use a short, relatable example.

Sample Answer:
A graph is a data structure made of nodes connected by edges, used to show relationships between items. It helps solve problems like navigation, social connections, or task planning.
For example, in a social network, users are nodes and their friendships are edges. Graphs make it easy to find mutual friends or suggest new connections.

Also Read: Types of Graphs in Data Structure & Applications

12. Can You Explain Singly and Doubly Linked Lists?

How to Answer:
Interviewers ask this to test your understanding of pointer manipulation and node relationships. Keep it clear. Define both types, mention when to use each, and highlight the trade-offs.

• Give a simple definition.
• Explain the practical difference.
• Use quick, real-world examples.

Sample Answer:
A singly linked list is a sequence of nodes where each node points to the next. It’s simple and efficient when you only need to move forward.
A doubly linked list has nodes that point both to the next and previous ones, allowing two-way traversal. It’s useful when you need quick insertions or deletions from both ends.
For example, a music playlist (forward-only) could use a singly linked list, while a browser’s back-and-forth navigation history suits a doubly linked list.

Also Read: Mastering Linked Lists in Data Structures

How to Answer:
Interviewers ask this to see if you can build a tree structure, apply insertion and search logic, and maintain proper ordering. Keep it focused. Define what a BST is, explain its core rules, and walk through a quick example.

• Give a clear definition.
• Highlight the key operations.
• Mention a practical use case.

Sample Answer:
A binary search tree is a structure where each node has up to two children. Left children hold smaller values, right children hold larger ones. You define a node class with value, left, and right. Then implement insert and search methods using recursion or loops.

For example, BSTs help speed up lookups in database indexing by organizing keys in a sorted, searchable form.

class Node:
   def __init__(self, key):
       self.key = key
       self.left = None
       self.right = None
class BST:
   def __init__(self):
       self.root = None
   def insert(self, key):
       def _insert(node, key):
           if not node:
               return Node(key)
           if key < node.key:
               node.left = _insert(node.left, key)
           else:
               node.right = _insert(node.right, key)
           return node
       self.root = _insert(self.root, key)
   def search(self, key):
       def _search(node, key):
           if not node or node.key == key:
               return node
           if key < node.key:
               return _search(node.left, key)
                           return _search(node.right, key)
        return _search(self.root, key)
class Node:
    def __init__(self, key):
        self.key = key
        self.left = None
        self.right = None

class BST:
    def __init__(self):
        self.root = None

    def insert(self, key):
        def _insert(node, key):
            if not node:
                return Node(key)
            if key < node.key:
                node.left = _insert(node.left, key)
            else:
                node.right = _insert(node.right, key)
            return node
        self.root = _insert(self.root, key)

    def search(self, key):
        def _search(node, key):
            if not node or node.key == key:
                return node
            if key < node.key:
                return _search(node.left, key)
            return _search(node.right, key)
        return _search(self.root, key)

Output:

Found node with key: 7
Key not found

13. Can You Explain Big O Notation and Its Importance in Coding?

How to Answer:
Interviewers ask this to see if you can evaluate how code performs as input scales. Keep it clear. Define Big O, explain its importance, and give a simple example.

• Give a clear definition.
• Explain why it matters in coding.
• Use an easy-to-follow example.

Sample Answer:
Big O notation describes how an algorithm's performance changes as the input size grows. It's important to write efficient code that can scale.

For example, a linear search goes through each item one by one — that's O(n). A binary search splits the list in half each time — that's O(log n), which is much faster for large, sorted lists.

Also Read: Algorithm Complexity and Data Structure: Types of Time Complexity

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:

• BFS is used in a navigation app to find the shortest route between two locations.
• DFS is used to validate task dependencies in a project to ensure there are no circular dependencies.

14. Can You Compare and Contrast Breadth-First Search and Depth-First Search?

How to Answer:
Interviewers ask this to assess your understanding of graph or tree traversal techniques. Stick to the core differences. Define each, explain when they’re useful, and give quick, relatable examples.

• Give a clear definition.
• Mention when each is typically used.
• Use simple, real-world examples.

Sample Answer:
 Breadth-First Search (BFS) explores all neighboring nodes level by level. It’s great for finding the shortest path in unweighted graphs.
 Depth-First Search (DFS) goes deep down one path before backtracking. It’s useful for detecting cycles or exploring all possible paths.

Example:
BFS is like finding the shortest route in a city map.
DFS is like solving a maze by following one path to the end before trying another.

Also Read: Difference Between DFS and BFS: DFS vs BFS, Similarities, and More

15. How Do You Optimize Algorithms for Better Performance?

How to Answer:
Interviewers ask this to gauge your thoughts on improving code efficiency. Keep it clear. Mention what optimization means and why it matters, and give a quick, relevant example.

• Define what algorithm optimization is.
• Mention common techniques.
• Give a simple, practical example.

Sample Answer:
Optimizing an algorithm means improving its time or space efficiency. It’s key for building fast, scalable applications.
For example, switching to a hashmap can cut search time from O(n) to O(1) instead of using a list for lookups.
In a Fibonacci problem, using memoization avoids redundant calculations and speeds up the solution.

Build your basics on data structures and algorithms strong so you can ace your next coding interview – enroll in upGrad’s free Data Structures & Algorithms course. Learn time complexity, basic data structures (Arrays, Queues, Stacks), and algorithms (Sorting, Searching) with just 50 hours of learning.

16. How to Find the First Non-Repeated Character in a String?

How to Answer:
Interviewers ask this to see how you approach string problems using frequency counts or indexing. Keep your explanation simple and focused. Mention the strategy, why it works, and show a quick example.

• Explain the core approach.
• Highlight why it's efficient.
• Use a short, clear example.

Sample Answer:
To find the first non-repeated character, scan the string and count how often each character appears. Then go through the string again and return the first character with a count of one.

For example, in the string "swiss", the first non-repeated character is 'w'.

Also Read: Top 13 String Functions in Java | Java String [With Examples]

18. How to Reverse Words in a Sentence Without Using Library Functions?

How to Answer:
Interviewers ask this to see if you can manipulate strings manually without relying on built-in helpers. Keep your explanation focused. Walk through your logic clearly and show that you can handle edge cases.

• Explain the manual process of splitting and reversing.
• Highlight that you're not using built-in functions.
• Keep the example straightforward and readable.

Sample Answer:
To reverse the words in a sentence without using library functions, you can manually extract each word, store them, and rebuild the sentence in reverse order. This shows control over string parsing and attention to detail.

For example, if the input is "hello world", the output should be "world hello".

Code Snippet and Explanation:
This code extracts words individually, stores them in a list, and then reverses their order to rebuild the sentence without using split() or join().

def reverse_words(sentence):
   words = []
   word = ""
   for char in sentence:
       if char == " ":
           words.append(word)
           word = ""
       else:
           word += char
   words.append(word)
   reversed_sentence = ""
   for i in range(len(words) - 1, -1, -1):
       reversed_sentence += words[i] + " "
   return reversed_sentence.strip()

Input

sentence = "hello world"

Output

print(reverse_words(sentence))  # Output: "world hello"

20. How to Determine if Two Strings are Rotations of Each Other?

How to Answer:
Interviewers ask this to see if you can apply a simple but smart string manipulation technique. Focus on clarity. Define the concept, explain the logic briefly, and give a quick example.

• Give a clear definition.
• Explain the logic behind the approach.
• Use a short, relatable example.

Sample Answer:
Two strings are rotations of each other if one can be transformed into the other by shifting characters. To check, concatenate the first string to itself and see if it contains the second string.

For example, if s1 is "abcd" and s2 is "dabc", then "abcdabcd" contains "dabc", so they are rotations.

20. How to Find All Permutations of a Given String?

How to Answer:
Interviewers ask this to test your understanding of recursion and backtracking. Keep your explanation simple. Describe the approach, highlight key considerations like repeated characters, and give a quick example.

• Explain the basic idea.
• Mention recursion and backtracking.
• Use a short, clear example.

Sample Answer:
To find all permutations of a string, I use recursion and backtracking. I fix one character and swap it with the rest, then repeat the process for the remaining characters.

For example, with "abc", the permutations are: "abc", "acb", "bac", "bca", "cab", and "cba".How to Answer:
Interviewers ask this to see if you can write reliable, fault-tolerant code. Keep it straightforward. Define what exception handling is, explain why it's important, and give a quick example.

• Give a clear definition.
• Mention why it matters.
• Use a simple, real-world example.

Sample Answer:
 Exception handling helps manage errors without crashing the program. I use try blocks for risky code, except blocks to catch specific errors, and finally to clean up resources.
For example, when reading a file, I catch FileNotFoundError and make sure to close the file afterward to avoid resource leaks.

Also Read: Top 32 Exception Handling Interview Questions and Answers in 2025 [For Freshers & Experienced]

20. How Do You Handle Exception Handling in Your Code?

How to Answer:
Interviewers ask this to see if you can write reliable, fault-tolerant code. Keep it straightforward. Define what exception handling is, explain why it's important, and give a quick example.

• Give a clear definition.
• Mention why it matters.
• Use a simple, real-world example.

Sample Answer:
 Exception handling helps manage errors without crashing the program. I use try blocks for risky code, except blocks to catch specific errors, and finally to clean up resources.
For example, when reading a file, I catch FileNotFoundError and make sure to close the file afterward to avoid resource leaks.

Also Read: Top 32 Exception Handling Interview Questions and Answers in 2025 [For Freshers & Experienced]

21. How to Implement a Queue Using Two Stacks?

How to Answer:
Interviewers ask this to see if you can creatively use basic data structures. Show that you understand how to keep the queue’s FIFO order using stacks, which are LIFO by nature. Explain the process simply and focus on the key steps.

• Explain the idea of using two stacks for enqueue and dequeue.
• Mention transferring elements to reverse order when needed.
• Keep it straightforward and easy to follow.

Sample Answer:
To implement a queue with two stacks, use one stack to add items (enqueue) and the other to remove items (dequeue). When the dequeue stack is empty, move all items from the enqueue stack to it—this reverses the order, so you get the correct FIFO behavior. This way, you maintain queue operations using only stacks.

22. Can You Write Code to Find the Maximum Depth of a Binary Tree?

How to Answer:
Interviewers ask this to see if you understand how to work with trees using recursion or level-order traversal and if you handle edge cases like null nodes. Calculating depth is a basic but important tree operation.

• Explain the approach simply.
• Mention why recursion fits well here.
• Walk through the logic briefly.

Sample Answer:
Yes, I can. To find the maximum depth of a binary tree, you check the depth of the left and right subtrees recursively. Starting at the root, you add 1 for each level until you reach the leaves (nodes with no children). The maximum depth is the larger depth between the left and right subtrees.

Here’s an example in Python:

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

This code returns 3 because the deepest path (1 → 2 → 4 or 1 → 2 → 5) has three nodes.

23. Understanding Recursion with Practical Examples – Explain How?

How to Answer:
Interviewers ask this to see if you understand how recursion works and can break problems down into smaller parts. Keep it clear and practical. Define recursion, explain why it’s useful, and give simple examples.

• Give a clear definition.
• Mention why it’s important.
• Use practical, easy-to-understand examples.

Sample Answer:
Recursion is when a function calls itself to solve smaller versions of the same problem until it reaches a base case. It’s useful for simplifying complex problems by breaking them down into manageable steps.

For example, calculating a factorial uses recursion by multiplying a number by the factorial of the previous number until it reaches 1. Another example is traversing a tree structure, where recursion helps visit each node systematically.

Example Code (Factorial in Python):

def factorial(n):
    if n == 1:  # base case
        return 1
    else:
        return n * factorial(n - 1)  # recursive call

print(factorial(5))

Output: 

120

Also Read: Python Recursive Function Concept: Python Tutorial for Beginners

24. Implementing Modern Sorting Algorithms – Explain How?

How to Answer:
Interviewers ask this to see if you understand key sorting algorithms that use divide-and-conquer techniques and how they perform efficiently. Keep it simple: briefly explain the algorithms, their approach, and why they’re useful.

• Name the algorithms.
• Explain their basic idea.
• Mention why they’re efficient.

Sample Answer:
Modern sorting algorithms like Merge Sort and Quick Sort are commonly used because they efficiently sort data with an average time complexity of O(n log n).

Merge Sort divides the array into two halves, sorts each half recursively, and then merges the sorted halves back together. Quick Sort selects a pivot element, partitions the array into values less than and greater than the pivot, and recursively sorts these partitions. Both use divide-and-conquer to speed up sorting compared to simpler methods.

25. How to Work with Dynamic Programming Problems?

How to Answer:
Interviewers ask this question to see if you understand how to optimize problems by reusing solutions to overlapping subproblems, either through memoization or bottom-up tabulation. Dynamic programming (DP) is essential for efficiently solving complex optimization and combinatorial problems.

• Explain what DP is.
• Mention why it’s essential.
• Give a simple example.

Sample Answer:
Dynamic programming is a technique where you solve problems by breaking them into smaller subproblems and storing their results to avoid redundant calculations. This makes your solution faster and more efficient.

For example, calculating the Fibonacci sequence with memoization stores previously computed values. Instead of recalculating them repeatedly, you reuse the stored results, significantly speeding up the process.

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]
n = 6
print(fibonacci(n)) 

Output

8

Here, for n = 6, the function returns 8, which is the 6th Fibonacci number, showing how DP optimizes the calculation.

Let me know if you want it more technical or simplified!

26. Can You Explain the Concept of Time Complexity with Real Examples?

How to Answer:
Interviewers ask whether you can relate Big O notation to real-life coding situations. Don’t just recite definitions—connect them to practical examples.

• Define time complexity simply.
• Mention why it matters.
• Give 1–2 quick, relatable examples.

Sample Answer:
Time complexity tells you how an algorithm’s runtime grows as the input gets larger. It helps you choose the most efficient solution.

For example, accessing an array element by index is O(1)—instant, no matter how big the array is.

arr = [10, 20, 30, 40]
print(arr[2])

Output

30

Finding the max in an unsorted list is O(n) because you have to scan every element.

arr = [3, 5, 1, 7, 9]
print(max(arr)) 

Output: 

9

So, time complexity helps you compare solutions and write faster code, especially for large datasets.

27. What Are the Differences Between Procedural and Functional Programming?

How to Answer:
Interviewers ask this to see if you understand different programming paradigms and when to use them. Keep your answer clear and practical. Define both styles, highlight key differences, and use a relatable example.

• Give a simple definition of each.
• Mention a few core differences.
• Use a real-world analogy or quick example.

Sample Answer:
Procedural programming is about writing step-by-step instructions that change program state. Functional programming focuses on using pure functions without changing data or relying on state.

For example, in procedural code, you might loop through a list and update values. In functional code, you’d use map() to create a new list without altering the original.

Understanding both helps you write cleaner, more flexible code depending on the task.

29. Can You Explain the Use of NoSQL Databases Over SQL Databases?

How to Answer:
Interviewers ask this to see if you understand how to work with different types of data and scale systems effectively. Stick to the basics. Highlight the difference, mention where each shines, and give a quick, real-world example.

• Give a clear comparison.
• Mention why it matters.
• Use a quick, practical example.

Sample Answer:
 SQL databases use structured data with fixed schemas, great for complex queries and relationships. NoSQL works better for unstructured or semi-structured data, offering more flexibility and easier scaling.

For example, SQL is ideal for banking systems where data relationships are strict. NoSQL fits social platforms where user content is varied and constantly growing.

Struggling with slow, inefficient SQL queries? Level up with upGrad’s Advanced SQL: Functions and Formulas to write high-performance, insight-driven queries. Learn MySQL, window functions, and complex formulas in just 11 hours!

Also Read: Is SQL Hard to Learn? Breaking Down the Challenges and Solutions

30. How Do You Ensure Your Code is Readable and Maintainable by Others?

How to Answer:
Interviewers ask this to see if you write code that others can easily understand and work with. Keep it practical. Mention a few habits, explain why they matter, and give a quick example.

• Mention a few key practices.
• Explain why they're important.
• Add a short, relatable example.

Sample Answer:
I make my code easy to read and maintain by using clear names, breaking logic into small functions, and adding comments when needed. It helps teammates understand and update the code faster. For example, I’d write calculateTotalPrice() instead of func1, and separate the discount logic into its own function for clarity.

31. How Can You Explain a Complex Technical Concept to a Non-Technical Person?

How to Answer:

Interviewers ask this to see if you can break down technical ideas clearly and relate them to everyday concepts. Focus on clarity, relevance, and relatability.

• Choose a standard technical term.
• Use a simple analogy or everyday example.
• Keep the explanation short and clear.

Sample Answer:

Here’s how I’d explain an API to someone without a tech background:

Think of an API like a coffee shop menu. You place an order by choosing a drink (request), the barista prepares it behind the counter (server), and then hands it to you (response). You don’t need to know how it’s made—you just get what you requested.

32. How Do You Approach Debugging a Difficult Issue?

How to Answer:
Interviewers ask this to see how you handle complex issues under pressure. Focus on showing a calm, logical approach. Walk through your steps briefly and clearly.

• Explain your general strategy.
• Mention key tools or techniques you use.
• Highlight how you stay focused and avoid guesswork.

Sample Answer:
When I encounter a challenging bug, I start by reproducing it to understand what’s going wrong. Then, I narrow down the possible causes by checking logs and isolating the code involved. I use print statements or a debugger to trace what’s happening, and I try small, targeted changes to fix it while keeping track of what I test.

Also Read: What is Debugging in Coding: Tools & Techniques for Debugging Explained

33. What is the Importance of Version Control Systems Like Git?

How to Answer:
Interviewers ask this to see if you understand how modern teams collaborate, manage changes, and maintain clean workflows. Keep it simple. Explain what version control is and why it's useful, and give a quick, real-world example.

• Give a clear definition.
• Mention why it's important.
• Use a quick, relatable example.

Sample Answer:
Version control systems like Git help track code changes and support collaboration across teams. They’re important for managing updates, resolving conflicts, and rolling back safely if something breaks.
For example, Git lets multiple developers work on different features in separate branches and later merge them without losing each other’s work.

Top 6 Coding Questions for Experienced Professionals

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: 

• System Design & Architecture: Making high-level decisions that impact performance, scalability, and reliability.
• Advanced Code Quality & Design Patterns: Applying and adapting design principles to optimize team velocity and maintainability.
• Technical Leadership & Mentorship: Communicating effectively, guiding project roadmaps, and ensuring best practices across teams.
• Strategic Decision-Making: Balancing trade-offs in technology, cost, deadlines, and product requirements for real-world outcomes.

Now, let’s explore the most crucial 6 coding interview questions with solutions for experienced professionals. 

1. Can You Explain the SOLID Principles in Software Development?

How to Answer:
Interviewers ask this to see if you can design software that’s easy to maintain and scale. Keep it simple—define SOLID, explain why it’s useful, and give a quick example.

• Give a clear definition.
• Mention why it’s important.
• Use a quick, relatable example.

Sample Answer:
The SOLID principles are five guidelines that help create clean and maintainable object-oriented code:

• Single Responsibility Principle: A class should have one job.
• Open/Closed Principle: Code should be open to adding new features but closed to changing existing code.
• Liskov Substitution Principle: Subtypes must work in place of their base types without issues.
• Interface Segregation Principle: Don’t force clients to depend on methods they don’t use.
• Dependency Inversion Principle: High-level modules should depend on abstractions, not on low-level details.

These principles help teams build scalable and bug-free software. For example, following the Single Responsibility Principle, one class might handle invoice data, while a separate class handles printing the invoice.

2. What Programming Languages Should You Know in 2025?

How to Answer:
Interviewers ask this to see if you’re aware of current and upcoming trends in programming and if you’re ready to adapt to new technologies. Keep your answer focused on the most relevant languages in 2025 and explain why they matter.

• Mention the key languages you should know.
• Explain briefly what they’re used for.
• Highlight why staying updated is essential.

Sample Answer:
In 2025, it’s essential to know these programming languages:

•  Python is widely used for data science, AI, and backend development.
•  JavaScript remains essential for both frontend and backend web development.
• Java is popular for enterprise applications and Android development.
• Go is gaining traction for building scalable backend services.
• Rust is growing in use for performance-critical applications.

Knowing these languages shows that you’re prepared for the evolving tech landscape and ready to tackle various projects.

3. Can You Describe a Challenging Project You Worked On and How You Overcame Obstacles?

How to Answer:
Interviewers ask this to understand how you deal with challenges and solve problems. Focus on the situation, the obstacles you faced, and the specific steps you took to overcome them.

• Briefly describe the project and the main challenge.
• Explain the approach or solution you used.
• Highlight the positive outcome or improvement.

Sample Answer:
In a recent project, I helped build an e-commerce site. The biggest challenge was slowing database queries during high traffic. I improved performance by analyzing query issues, adding indexes, and caching frequent requests, which cut response times by half.

4. How Do You Keep Your Coding Skills Up to Date?

How to Answer:
Interviewers ask whether you have a growth mindset and stay current with technology. Focus on how you actively learn and improve your skills. Mention specific ways you keep up to date without sounding generic.

• Explain your approach to continuous learning.
• Mention resources or methods you use.
• Give a recent example if possible.

Sample Answer:
I keep my coding skills current by regularly taking online courses and reading tech blogs. For example, I recently completed a course on cloud architecture to deepen my knowledge of AWS and Azure. I also follow platforms like Medium and Dev.to to stay informed about new tools and best practices.

Also Read: Top 20 Uses of AWS: How Amazon Web Services Powers the Future of Cloud Computing

5. Can You Discuss a Time When You Had to Learn a New Technology Quickly?

How to Answer:
Interviewers ask this to see how you handle learning new tools or technologies under pressure. Focus on your approach to quickly understanding something unfamiliar and applying it effectively.

• Explain your learning process.
• Mention how you found resources.
• Highlight the impact of your quick learning.

Sample Answer:
I explore official documentation and tutorials to build a solid foundation when faced with a new technology. I also look for practical examples or community discussions to deepen my understanding. Recently, I had to learn Jenkins for setting up CI/CD pipelines. Within a week, I mastered the basics, implemented the pipeline, and helped reduce deployment time by 30%.

Also Read: Top Jenkins Project Ideas for Beginners & Experts | Build & Automate

6. Can You Explain Design Patterns and Provide Examples?

How to Answer:
Interviewers ask this to see if you know how to use proven solutions to common coding problems. Keep it straightforward. Define design patterns, explain why they matter, and give simple examples.

• Give a clear definition.
• Explain their purpose.
• Provide quick, familiar examples.

Sample Answer:
Design patterns are standard solutions to common software design problems. They help make code more reusable and easier to manage. For example, the Singleton pattern ensures only one instance of a class exists, while the Observer pattern lets objects get notified when something changes.

What Are the Latest Trends in Coding Interviews?

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:

1. Virtual Interviews are the Norm: Remote and hybrid interviewing has become standard. This means candidates should be comfortable coding in shared online editors and communicating effectively over video, as many hiring processes remain fully or partially virtual even for major employers.
2. Higher Bar & Competition: With an abundance of applicants in the market, companies have raised their hiring bar. Technical interviews have grown more selective – data shows candidates must perform roughly 22% better in coding assessments now than a couple of years ago to land an offer​.
3. Algorithms Still Reign: Data structures and algorithms remain a cornerstone of technical rounds. Many companies (from startups to FAANG) continue to rely on LeetCode-style coding challenges as a standardized filter for problem-solving skills​.
4. System Design Emphasis: There’s a stronger focus on system design interviews, especially for mid-level and senior engineering roles. Employers want to see that candidates can architect scalable systems and discuss trade-offs. In many onsite loops, you may be asked to design a complex system from scratch.
5. Structured Behavioral Rounds & Soft Skills: Behavioral interviews have become more structured and significant. Companies probe for teamwork, leadership, and problem-solving approaches using standardized questions (often following the STAR method). In fact, 92% of employers say that soft skills are as important as or more important than technical skills​.
6. AI’s Growing Influence: The advent of AI tools like ChatGPT is subtly changing interview dynamics. Companies are wary of AI-assisted cheating on standard questions, so some are moving away from trivial or well-known problems that a quick prompt can solve​.

What are Some Tips for Cracking the Coding Interview? Top 5 Tips 

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.

1. Understanding the Basics: Build a strong foundation in data structures (arrays, linked lists, stacks, queues) and algorithms (sorting, searching) to tackle complex problems confidently.
2. Regular Practice and Problem-Solving: Practice coding daily. Focus on a mix of easy, medium, and hard problems to develop both speed and accuracy.
3. Mock Interviews and Time Management: Conduct mock interviews and solve problems within time limits to improve efficiency and articulate your thought process effectively.
4. Staying Updated with Industry Trends: Learn trending technologies, languages, and frameworks like Python for AI or Go for cloud-native apps to stay industry-relevant.

Enhancing Communication Skills: Practice explaining your solutions clearly and step-by-step, as interviewers value both coding and communication skills.

How upGrad can Boost your Interview?

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.Stay consistent, focus on optimizing solutions, and keep refining your knowledge to excel in your next interview.
 

upGrad’s programming courses offer — an opportunity to master coding, build real-world projects, and stay ahead in the competitive tech industry.

Here are some of our  additional software development courses that will take you a long way:

• Professional Certificate Program in Cloud Computing and DevOps
• Professional Certificate Program in UI/UX Design & Design Thinking
• Executive Diploma in Machine Learning and AI with IIITB
• If you're ready to take the next step in your career, connect with upGrad’s career counseling for personalized guidance. You can also visit a nearby upGrad center for hands-on training to enhance your generative AI skills and open up new career opportunities!

You can explore more software development courses by upGrad.

Boost your career with our popular Software Engineering courses, offering hands-on training and expert guidance to turn you into a skilled software developer.

Explore our Popular Software Engineering Courses

Master in-demand Software Development skills like coding, system design, DevOps, and agile methodologies to excel in today’s competitive tech industry.

In-Demand Software Development Skills

Stay informed with our widely-read Software Development articles, covering everything from coding techniques to the latest advancements in software engineering.

Read our Popular Articles related to Software