Cracking the Coding Interview with 65 Coding Questions in 2025

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.

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 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:

• 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. 

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

1. What is a Data Structure?

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.

• Efficient storage: Organizes data for quick access.
• Optimized algorithms: Simplifies solving complex problems.
• Dynamic handling: Structures like linked lists grow and shrink dynamically.
• Faster operations: Accelerates search and update processes.

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.

2. What is an Array and How is it Used?

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.

• Organized storage: Groups-related data, like exam marks.
• Direct access: Access elements via index.
• Efficient: Ideal for fixed-size data, e.g., tables.

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.

3. What is a Linked List and its Applications?

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.

1. Data: The value stored in the node.
2. Pointer: A reference to the next node in the sequence.

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. 

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

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.

Example: If you’re implementing a student attendance tracker:

• Use an array for a static classroom where the number of students is fixed.
• Use a linked list if the class size changes frequently.

Code Snippet and Explanation:

This code compares arrays and linked lists for managing student lists. 

• Arrays handle static lists efficiently, offering instant access to elements and easy addition when the size is fixed. 
• Linked lists excel with dynamic lists, enabling flexible addition or removal of elements without memory constraints, making them ideal for unpredictable sizes. 

# 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:

• Array: 

["Sneha", "Dinesh", "Arup"]

• Linked List:

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

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

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:

• Class: Product defines attributes like name and price.
• Object: Instances like Laptop or Smartphone from the Product class.
• Encapsulation: Restricts access to sensitive data like price.
• Inheritance: Clothing class inherits Product properties, adding unique features like size.

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

6. What are Classes and Objects in OOP?

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.

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

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). 

• Child classes ‘Car’ and ‘Motorcycle’ inherit these, adding unique attributes (airbags, handlebars) and customizing methods. 
• Objects showcase inheritance, overriding, and class-specific functionality, like displaying safety features or attributes.

# 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: 

• A Car object with brand "Toyota", model "Camry", and 6 airbags.
• A Motorcycle object with brand "Harley-Davidson", model "Street 750", and type "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?

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.

• It Simplifies Code: General code works with multiple objects.
• It Promotes Reusability: Reduces repetition.
• It Enhances Flexibility: Adapts to changes easily.

Example: Think of a “perform” button on different types of devices.

• On a TV, it opens a streaming app.
• On a smart speaker, it plays music.

The button is the same (shared interface), but its behavior depends on the device (object) you’re interacting with.

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

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.

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

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

11. Can You Explain Conditional Statements with Examples?

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. 

• If seats are available, it prints the availability. 
• If not, but the customer has special needs, it prioritizes them. 
• Otherwise, it informs customers that no seats are available.

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?

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: 

• For Loop: Calculating the average of numbers in a list.
• While Loop: Continuously prompting a user to input valid data until they provide it.

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

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:

1. Base Case: The condition that stops recursion. Without this, the program runs infinitely.
2. Recursive Case: The function calls itself with a smaller problem.

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

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

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"

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

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.

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?

Why Do Interviewers Ask This Question?

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:

• Hierarchical Data: Represent structures like file systems.
• Expression Parsing: Evaluate mathematical expressions (e.g., in calculators).

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

Why Do Interviewers Ask This Question?

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:

1. The left child contains nodes with values less than the parent node.
2. The right child contains nodes with values greater than the parent node.

Here’s how they work.

• When searching for a value, you compare it with the root node. 
• If it’s smaller, move to the left child; if larger, move to the right. 

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.

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

Why Do Interviewers Ask This Question?

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:

• Linear Data Structure: Arrays allow direct access using indices, making them ideal for ordered data.
• Non-linear Data Structure: A tree organizes data into parent-child relationships, such as representing a company's hierarchy

4. How Would You Implement the Bubble Sort Algorithm?

Why Do Interviewers Ask This Question?

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. 

• It makes multiple passes through the list, ensuring that the largest unsorted number moves to its correct position with each pass. 
• The process continues until the entire list is sorted.

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. 

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

Why Do Interviewers Ask This Question?

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.

• You start with one card (already sorted).
• Pick the next card and place it in the correct position relative to the first.
• Continue picking cards one by one and inserting them into the correct position among the already sorted cards.

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

Why Do Interviewers Ask This Question?

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. 

• If the middle element is smaller, search the right half
• If the middle element is larger, search the left half. 

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.

• “Is it greater than 50?”
• “Is it less than 25?”
• Until you find the number.

This is exactly how binary search works!

7. What is the Best Sorting Algorithm and Why?

Why Do Interviewers Ask This Question?

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(nlog⁡n)O(n \log n)O(nlogn).

• Merge Sort: Stable and efficient for large datasets but requires additional memory.
• Quick Sort: Faster for most datasets but can degrade to O(n2)O(n^2)O(n2) if the pivot is poorly chosen.

For nearly sorted data, Insertion Sort or Bubble Sort may perform better due to their simplicity.

8. Can You Print a Fibonacci Sequence Using Recursion?

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:

1. Start with 0 and 1.
2. Add them to get the next number (1).
3. Repeat: 1+1=2, 1+2=3, etc.

Code Snippet and Explanation:

This code calculates each Fibonacci number by calling the Fibonacci function recursively. 

• For each term, it adds the two previous terms until it reaches the base case (0 or 1). 
• The sequence builds step by step to generate the desired output.

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.

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

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).

upGrad’s Exclusive Software and Tech Webinar for you –

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

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). 

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

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:

1. Navigation Systems: Finding the shortest path between locations.
2. Social Networks: Modeling connections between users.
3. Dependency Resolution: Managing task dependencies in project management software.

Example: Graphs are used in social networks like Facebook, where nodes represent users and edges represent friendships or connections. 

12. Can You Explain Singly and Doubly Linked Lists?

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:

1. Singly Linked List: For lightweight applications where only forward traversal is needed.
2. Doubly Linked List: For applications requiring frequent insertions/deletions from both ends or reverse traversal.

Also Read: Mastering Linked Lists in Data Structure

13. How to Implement a Binary Search Tree?

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.

• Define a Node class with attributes for storing the value (key) and pointers to its left and right children.
• Create methods to handle insert, search, and optionally delete operations while maintaining the BST property.
  • The left subtree contains values smaller than the node.
  • The right subtree contains values greater than the node.

Example: Binary Search Trees are used in database indexing, like finding a record in a database table quickly by organizing keys hierarchically.

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

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:

• Linear Search: O(n)O(n)O(n) — checks each element until the value is found.
• Binary Search: O(log⁡n)O(\log n)O(logn) — halves the search space each step, much faster for large sorted datasets.

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

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.

16. How Do You Optimize Algorithms for Better Performance?

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:

1. Choosing Efficient Data Structures: Use hashmaps for quick lookups instead of lists.
2. Eliminating Redundancy: Use memoization or caching to avoid repeating expensive calculations.
3. Breaking Down Problems: Apply divide-and-conquer approaches to simplify complex tasks.
4. Parallel Processing: Execute parts of the algorithm concurrently where possible.

Example: Optimizing a recommendation system by caching user preferences reduces repeated database queries, improving response time significantly.

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

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'.

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

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:

• Split the sentence into individual words.
• Reverse the order of the words.
• Combine them back into a single string.

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"

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

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.

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

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:

• Fix one character and recursively find permutations of the rest.
• Repeat this for each character in the string.

Example: For "abc", the permutations are ["abc", "acb", "bac", "bca", "cab", "cba"].

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

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. 

• Use try blocks to wrap code that may throw an error, and catch or except blocks to handle those errors. 
• Always ensure proper resource cleanup with finally blocks.

22. How to Implement a Queue Using Two Stacks?

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:

1. Use one stack (stack1) for enqueue operations (adding elements).
2. Use the other stack (stack2) for dequeue operations (removing elements).
3. When stack2 is empty during dequeue, transfer elements from stack1 to reverse their order.

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

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. 

• Starting from the root, it adds 1 for each level of the tree until it reaches the leaf nodes (nodes with no children). 
• The largest depth among the left and right subtrees is returned as the final result.

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]

24. Understanding Recursion with Practical Examples – Explain How?

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.

• Calculating Factorial: Used in mathematical computations, such as permutations and combinations.
• Tower of Hanoi: Used in problems involving disk movement puzzles.
• Tree Traversal: Recursion is extensively used in traversing data structures like binary trees.

25. Implementing Modern Sorting Algorithms – Explain How?

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(nlog⁡n)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]

26. How to Work with Dynamic Programming Problems?

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

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

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.

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

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.

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

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:

• SQL Use Case: Banking systems that require strict relationships between tables (e.g., accounts and transactions).
• NoSQL Use Case: Social media platforms that manage unstructured posts, comments, and user interactions across distributed servers.

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

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:

1. Use meaningful variable and function names.
2. Add comments where necessary to explain the logic.
3. Follow consistent coding standards.
4. Break complex logic into smaller, reusable functions.
5. Write unit tests to validate functionality.

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

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."

32. How Do You Approach Debugging a Difficult Issue?

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:

1. Reproduce the problem to understand its behavior.
2. Isolate the code or components causing the issue.
3. Use tools like debuggers or logs to trace the error.
4. Test possible fixes incrementally and document my findings.

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

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.

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?

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:

1. Single Responsibility Principle (SRP): A class should have one and only one reason to change.
2. Open/Closed Principle (OCP): Software entities should be open for extension but closed for modification.
3. Liskov Substitution Principle (LSP): Subtypes must be substitutable for their base types.
4. Interface Segregation Principle (ISP): Clients should not be forced to depend on methods they don’t use.
5. Dependency Inversion Principle (DIP): High-level modules should not depend on low-level modules; both should depend on abstractions.

Example: Applying SRP, a class Invoice should only handle invoice details, while a separate class InvoicePrinter should handle printing invoices.

2. What Programming Languages Should You Know in 2025?

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:

1. Python: For data science, AI, and backend development.
2. JavaScript: For web development (frontend and backend).
3. Java: For enterprise applications and Android development.
4. Go: For scalable backend services.
5. Rust: For performance-critical applications.

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

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%."

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

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."

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

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%."

6. Can You Explain Design Patterns and Provide Examples?

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:

• Singleton Pattern: Ensures only one instance of a class is created.
• Observer Pattern: Notifies multiple objects of a change in another object.

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 to Boost Your Career with upGrad's Programming Courses?

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:

• Professional Certificate Program in Cloud Computing and DevOps: Starts on March 31, 2025
• Professional Certificate Program in UI/UX Design & Design Thinking: Admissions on the roll.
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You can explore more software development courses by upGrad.

Conclusion

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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