Coding Interview: Top 70 Questions and Answers

Introduction

Coding interviews are a key part of getting hired as a software developer. They are not only about solving problems but also about how you think, how you write code, and how clearly you explain your approach.

With more people aiming for tech roles, interview preparation has become more common than ever. In fact, according to Data Intelo, the coding interview prep market was valued at around USD 2.13 billion in 2024 and is expected to continue growing through 2033. To perform well in these interviews, it helps to understand what interviewers usually focus on.

Some of the areas you’ll often face in coding interviews include:

• Data structures and algorithms
• Problems based on arrays, strings, and linked lists
• Questions involving recursion and dynamic programming
• Sorting and searching concepts
• System design and performance optimization

This article covers common coding interview questions, what interviewers look for, and practical tips to prepare effectively. It is ideal for students, freshers, and professionals aiming for developer roles, and it helps boost confidence and improve their chances of securing software engineering positions.

Top Frequently Asked Coding Questions

Here are some of the most commonly asked coding questions across entry-level, mid-level, and even senior technical interviews. They test problem-solving skills, logical thinking, and core programming fundamentals.

1. Write a program to reverse a string.

Loop through the string from the end to the beginning, or use built-in reverse functions to create the reversed string.

2. Check whether a given number is prime.

A number is prime if it is greater than 1 and divisible only by 1 and itself.

3. Find the factorial of a number.

Factorial is calculated by multiplying all positive integers from 1 to the given number (n!).

4. Determine if a string or number is a palindrome.

Compare the original value with its reverse; if they match, it’s a palindrome.

5. Find the largest and smallest number in an array.

Traverse the array and track the maximum and minimum values during iteration.

6. Write a program to remove duplicate elements from an array.

Use a set or check each element before adding it to a new array to avoid duplicates.

7. Find the Fibonacci sequence up to n terms.

Generate numbers by adding the previous two numbers, starting from 0 and 1.

8. Check if two strings are anagrams.

Sort both strings and compare them, or count character frequencies to check if they match.

9. Find the second-largest element in an array.

Track the largest and second-largest values while iterating through the array.

10. Write a program to swap two numbers without using a third variable.

Swap values using arithmetic operations or XOR without requiring extra memory.

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Coding Interview Questions on Conceptual Understanding

This section covers some coding interview questions that test the candidate's conceptual understanding.

1. What is a Data Structure?

A data structure is a storage format that defines how data is stored, organized, and manipulated. Data structure makes it easy for data to be accessed and used efficiently. Some famous data structures are Arrays, Trees, and Graphs.

2. What is an Array?

An array is a data structure that stores a fixed-size sequence of elements. All the items stored belong to the same type (list of numbers or names). It organizes data so that related values can be easily sorted or searched.

Fig: Array

3. What is a Graph?

A graph in a data structure contains a set of ordered pairs, also known as edges or arcs. They are most commonly used to connect nodes where data can be stored and retrieved.

4. What is a Tree?

A tree is a hierarchical data structure used to represent data in a way that allows for easy traversal and organization. It is made up of nodes connected by edges, and it resembles an inverted tree with branches and leaves, which is why it’s called a tree.

5. What is a Linked List?

A linked list is a linear data structure in which the elements are not necessarily stored in a contiguous manner. It is a sequence of nodes containing a value and a reference to the next node. It’s like a chain, where each link points to the next one.

The various types of linked lists include singly linked lists, doubly linked lists, and circular linked lists..

Fig: Linked List

6. What are LIFO and FIFO?

LIFO stands for Last In First Out. It is a method of organizing data in which the last item added is the first one removed.

FIFO stands for First In First Out. It is a method of organizing data where the first item added is the first one to be removed.

Fig: LIFO, FIFO

7. What is a Stack?

A stack in a data structure performs operations in a LIFO (Last In First Out) order. In a stack, elements can only be accessed, starting from the topmost to the bottom element.

8. What is a Queue?

A queue in a data structure performs operations in a FIFO (First In First Out) order. In a queue, the least recently added elements are removed first, instead of stacking.

Fig: Queue

9. What are Binary Trees?

A binary tree is a data structure in which each item can have at most two children. You’ll see them a lot in searching, sorting, or when representing structured data like file systems.

Each node in a binary tree can have up to two children, usually called the left and right child. A node might have none, one, or both children, which makes the structure flexible for different problems.

Binary trees also serve as the starting point for more complex data structures, such as binary search trees, heaps, and expression trees.

Fig: Binary Trees

10. What is a Binary Search Tree?

A binary search tree stores data and retrieves it very efficiently.

• The left sub-tree contains nodes whose keys are less than the node’s key value.
• The right sub-tree contains nodes whose keys are greater than or equal to the node’s key value.

Fig: Binary Search Tree

11. What is Recursion?

Recursion refers to a function calling itself based on a terminating condition. It uses LIFO and, therefore, uses the stack data structure.

12. What is the OOPS concept?

OOPS stands for Object-Oriented Programming System, a paradigm that provides concepts such as objects, classes, and inheritance. It helps organize and structure code to represent real-world entities, their attributes, and the actions they can perform. OOPS helps make code more reusable, scalable, and easier to maintain.

Recommended Reads 📰:

1. OOPS in C++
2. OOPS in Python
3. OOPS in Java

13. What are the concepts introduced in OOPS?

The following are the concepts introduced in OOPS:

• Object: A real-world entity having a particular state and behavior. We can define it as an instance of a class.
• Class: A logical entity that defines the blueprint from which an object can be created or instantiated.
• Inheritance: A concept that refers to an object gaining all the properties and behaviors of a parent object. It provides code reusability.
• Polymorphism: A concept that allows a task to be performed differently. In Java, we use method overloading and method overriding to achieve polymorphism.
• Abstraction: A concept that hides the internal details of an application and only shows the functionality. In Java, we use abstract classes and interfaces to achieve abstraction.
• Encapsulation: A concept that refers to the wrapping of code and data together into a single unit.

14. What are Doubly Linked Lists?

Doubly linked lists are a particular type of linked list in which traversal across the data elements can be done in both directions. This is made possible by two links in every node: one linking to the node next to it and another connecting to the node before it.

Fig: Doubly Linked List

15. Differentiate between linear and non-linear data structures.

16. What is a Deque?

A deque is a double-ended queue. This is a structure in which elements can be inserted or removed from either end.

17. What’s the difference between a Stack and an Array?

Stack follows a Last In First Out (LIFO) pattern. This means that data access necessarily follows a particular sequence, where the last data stored is the first to be extracted.

On the other hand, arrays do not follow a specific order but can instead be accessed or called by referring to the indexed element within the array.

18. Which sorting algorithm is the best?

There are many types of sorting algorithms: bubble sort, quick sort, balloon sort, merge sort, radix sort, and more. No algorithm can be considered the best or fastest because it has been designed for a specific type of data structure where it performs the best.

19. How does variable declaration affect memory?

The amount of memory to be reserved or allocated depends on the data type stored in a variable. For example, if a variable is declared “integer type,” 32 bits of memory storage will be reserved for that particular variable.

20. What are dynamic data structures?

Dynamic data structures have the feature where they expand and contract as a program runs. It provides a very flexible data manipulation method because it adjusts based on the data size to be manipulated.

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Programming Interview Questions

Preparing for coding interviews requires a deep understanding of key concepts and problem-solving skills. We have organized the questions and answers into specific categories to guide your preparation. Whether you're tackling data structure challenges, refining your algorithms knowledge, or designing scalable systems with system design, each section provides targeted questions and solutions to help you think critically and perform well in interviews.

Data Structures-based Coding Interview Questions and Answers

Mastering data structures is essential for writing well-optimized and scalable code to solve complex problems. Understanding how data is stored, accessed, and altered helps developers build faster and more efficient solutions across diverse applications. Here are various coding interview questions related to arrays, linked lists, stacks, queues, trees, and graphs, helping you understand how to approach problems and implement optimal solutions.

Arrays

21. How do you reverse an array in place?

To reverse an array in place, you can use two pointers: one starting from the beginning and the other from the end of the array. Swap the elements at these pointers, then move the pointers towards each other until they meet.

function reverseArray(arr) {
let start = 0;
let end = arr.length - 1;
while (start < end) {
{arr[start], arr[end]] = [arr[end], arr[start]];
// Swap elements
start++;
end--;
}
return arr;
}

22. How do you find the K-th largest element in an array?

One way to find the K-th largest element is by sorting the array and selecting the element at the K-th position from the end. Alternatively, you can use a min-heap or quickselect algorithm for better performance (O(n) time complexity on average).

function findKthLargest(nums, k) {
nums.sort((a, b) => b - a); // Sort in descending order
return nums[k - 1]; // K-th largest element
}

23. How can you move all zeroes to the end of an array while maintaining the order of other elements?

You can solve this by iterating over the array and shifting non-zero elements to the left. Then, fill the remaining positions with zeros.

function moveZeroes(arr) {
let nonZeroIndex = 0;
// Move all non-zero elements to the beginning
for (let i = 0; i < arr.length; i++) {
if (arr[i] !== 0) {
arr[nonZeroIndex] = arr[i];
if (nonZeroIndex !== i) arr[i] = 0;
nonZeroIndex++;
}
}
return arr;
}

Linked Lists

24. How do you detect a cycle in a linked list?

Using Floyd’s Cycle-Finding Algorithm, the tortoise and hare algorithm, you can detect a cycle in a linked list. You use two pointers: one moves one step at a time (slow), and the other moves two steps at a time (fast). If there's a cycle, the fast pointer will eventually meet the slow pointer.

function hasCycle(head) {
let slow = head;
let fast = head;
while (fast !== null && fast.next !== null) {
slow = slow.next;
fast = fast.next.next;
if (slow === fast) {
return true; // Cycle detected
}
}
return false; // No cycle
}

25. How would you reverse a linked list?

To reverse a linked list, you can iterate through the list while changing the next pointer of each node to point to the previous node.

function reverseLinkedList(head) {
let prev = null;
let current = head;
while (current !== null) {
let nextNode = current.next;
current.next = prev;
prev = current;
current = nextNode;
}
return prev; // New head of the reversed list
}

26. How can you merge two sorted linked lists into a single sorted list?

You can merge two sorted linked lists by comparing the nodes one by one and linking them in sorted order.

function mergeTwoSortedLists(list1, list2) {
let dummy = new ListNode(0);
// Temporary node to hold the result
let current = dummy;
while (list1 !== null && list2 !== null) {
if (list1.val < list2.val) {
current.next = list1;
list1 = list1.next;
} else {
current.next = list2;
list2 = list2.next;
}
current = current.next;
}
// Attach remaining nodes
if (list1 !== null) {
current.next = list1;
} else {
current.next = list2;
}
return dummy.next; // Return merged list
}

Stacks

27. How do you implement a stack using two queues?

You can implement a stack using two queues: one for pushing elements and the other for popping. When pushing, enqueue the new element into the first queue. When popping, move all elements from the first queue to the second, leaving the last element to pop.

class Stack {
constructor() {
this.queue1 = [];
this.queue2 = [];
}
push(x) {
this.queue1.push(x);
}
pop() {
while (this.queue1.length > 1) {
this.queue2.push(this.queue1.shift());
}
let poppedElement = this.queue1.shift();
[this.queue1, this.queue2] = [this.queue2, this.queue1]; // Swap queues
return poppedElement;
}
top() {
return this.queue1[this.queue1.length - 1];
}
empty() {
return this.queue1.length === 0;
}
}

28. What is the method for evaluating a postfix expression?

To evaluate a postfix expression, you use a stack. For each token:

• Push numbers onto the stack.
• When you encounter an operator, pop the required operands from the stack, perform the operation, and push the result back onto the stack.

function evaluatePostfix(expression) {
let stack = [];  
for (let char of expression) {
if (!isNaN(char)) {
stack.push(parseInt(char)); // Push operands to stack
} else {
let b = stack.pop();
let a = stack.pop();
switch (char) {
case '+':
stack.push(a + b);
break;
case '-':
stack.push(a - b);
break;
case '*':
stack.push(a * b);
break;
case '/':
stack.push(a / b);
break;
}
}
}
return stack.pop(); // Final result
}

29. How can you implement a stack that supports push, pop, and retrieving the minimum element?

You use an auxiliary stack that stores the minimum values to implement a stack that supports retrieving the minimum element in constant time. Every time you push a new element, you push the minimum value between the new and previous minimum onto the auxiliary stack.

class MinStack {
constructor() {
this.stack = [];
this.minStack = [];
}
push(x) {
this.stack.push(x);
if (this.minStack.length === 0 || x <= this.minStack[this.minStack.length - 1]) {
this.minStack.push(x);
}
}
pop() {
let popped = this.stack.pop();
if (popped === this.minStack[this.minStack.length - 1]) {
this.minStack.pop();
}
}
top() {
return this.stack[this.stack.length - 1];
}
getMin() {
return this.minStack[this.minStack.length - 1];
}
}

Queues

30. How do you implement a queue using two stacks?

To implement a queue using two stacks, you use one stack for enqueueing (pushing elements) and the other for dequeueing (popping elements). When dequeuing, if the dequeue stack is empty, transfer all elements from the enqueue stack to the dequeue stack.

class MyQueue {
constructor() {
this.stack1 = [];
this.stack2 = [];
}
enqueue(x) {
this.stack1.push(x);
}
dequeue() {
if (this.stack2.length === 0) {
while (this.stack1.length > 0) {
this.stack2.push(this.stack1.pop());
}
}
return this.stack2.pop();
}
peek() {
if (this.stack2.length === 0) {
while (this.stack1.length > 0) {
this.stack2.push(this.stack1.pop());
}
}
return this.stack2[this.stack2.length - 1];
}
empty() {
return this.stack1.length === 0 && this.stack2.length === 0;
}
}

31. What is a circular queue, and how is it implemented?

A circular queue is a queue where the last position is connected back to the first. This design avoids wasted space that occurs in a standard queue after deletions.

Instead of shifting elements, a circular queue uses modulo arithmetic to move the front and rear pointers. When the end of the array is reached, the pointer wraps around to the beginning.

This structure is commonly used in scheduling systems and buffering scenarios.

32. How do you implement a queue using a linked list?

For the implementation of a queue using a linked list, 

• Use a Node class with 'data' and 'next'
• Use two pointers: front and rear
• On enqueue, add to rear
• On dequeue, remove from front

class Node {
int data;
Node next;
Node(int data) {
this.data = data;
this.next = null;
}
}
class Queue {
Node front, rear;
public void enqueue(int data) {
Node newNode = new Node(data);
if (rear == null) {
front = rear = newNode;
return;
}
rear.next = newNode;
rear = newNode;
}
public int dequeue() {
if (front == null) return -1;
int value = front.data;
front = front.next;
if (front == null) rear = null;
return value;
}
}

34. How do you group anagrams from a list of strings?

The grouping of anagrams from a list of strings is done as follows:

• Sort each string
• Use it as a key in a HashMap to group anagrams

import java.util.*;
public class GroupAnagrams {
public static List<List<String>> groupAnagrams(String[] strs) {
Map<String, List<String>> map = new HashMap<>();
for (String word : strs) {
char[] chars = word.toCharArray();
Arrays.sort(chars);
String key = new String(chars);
map.computeIfAbsent(key, k -> new ArrayList<>()).add(word);
}
return new ArrayList<>(map.values());
}
}

35. How would you find the intersection of two arrays using a hash map?

• Use a map to track elements of the first array
• Then, loop through the second to find matches

import java.util.*;
public class ArrayIntersection {
public static List<Integer> intersection(int[] nums1, int[] nums2) {
Map<Integer, Boolean> map = new HashMap<>();
for (int num : nums1) {
map.put(num, true);
}
List<Integer> result = new ArrayList<>();
for (int num : nums2) {
if (map.getOrDefault(num, false) && !result.contains(num)) {
result.add(num);
}
}
return result;
}
}

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Trees

36. How do you perform an inorder traversal of a binary tree?

You have to follow the below path to perform an inorder traversal of a binary tree:

Traverse the left subtree → visit root → traverse the right subtree.

void inorderTraversal(TreeNode root) {
if (root == null) return;
inorderTraversal(root.left);
System.out.print(root.val + " ");
inorderTraversal(root.right);
}

37. How can you check if a binary tree is balanced?

A binary tree is considered balanced if, for every node, the height difference between the left and right subtrees is not more than one.

To check this, the tree is traversed recursively. At each node, the height of both subtrees is calculated. If the difference exceeds one at any point, the tree is unbalanced.

This approach ensures the tree maintains efficient performance for operations like search and insert.

38. How do you find the lowest common ancestor (LCA) of two nodes in a binary search tree?

If both values are smaller/larger than the root, move left/right. Else, the root is the LCA.

TreeNode lowestCommonAncestor(TreeNode root, TreeNode p, TreeNode q) {
if (root.val > p.val && root.val > q.val)
return lowestCommonAncestor(root.left, p, q);
else if (root.val < p.val && root.val < q.val)
return lowestCommonAncestor(root.right, p, q);
else
return root;
}

Graphs

39. How do you implement depth-first search (DFS) in a graph?

We can implement depth-first search (DFS) in a graph through these steps:

• Use a visited set to track explored nodes.
• Recursively explore neighbors starting from a node.

import java.util.*;
public class GraphDFS {
private Map<Integer, List<Integer>> adj = new HashMap<>();
public void addEdge(int u, int v) {
adj.computeIfAbsent(u, k -> new ArrayList<>()).add(v);
}
public void dfs(int start, Set<Integer> visited) {
if (visited.contains(start)) return;
visited.add(start);
System.out.print(start + " ");
for (int neighbor : adj.getOrDefault(start, new ArrayList<>())) {
dfs(neighbor, visited);
}
}
}

40. How can you find the shortest path in an unweighted graph using BFS?

BFS analyzes nodes level by level, making finding the shortest path in an unweighted graph simple. BFS guarantees the shortest path or the fewest edges in an unweighted graph.

void bfsShortestPath(Map<Integer, List<Integer>> graph, int start) {
Queue<Integer> queue = new LinkedList<>();
Set<Integer> visited = new HashSet<>();
Map<Integer, Integer> distance = new HashMap<>();
queue.add(start);
visited.add(start);
distance.put(start, 0);
while (!queue.isEmpty()) {
int node = queue.poll();
for (int neighbor : graph.getOrDefault(node, new ArrayList<>())) {
if (!visited.contains(neighbor)) {
queue.add(neighbor);
visited.add(neighbor);
distance.put(neighbor, distance.get(node) + 1);
}
}
}
// Print shortest distances
for (Map.Entry<Integer, Integer> entry : distance.entrySet()) {
System.out.println("Node " + entry.getKey() + ", Distance: " + entry.getValue());
}
}

41. How do you detect if a graph contains a cycle?

In line with the graph type, there are two main approaches. 

• Undirected Graph (using DFS): You start DFS from any node and keep track of visited nodes and their parent. If, during DFS, you find a visited node that is not the parent, a cycle exists.
• Directed Graph (using DFS and recursion stack): You have to use two sets: one for visited nodes and one for the recursion stack. A cycle exists if you find a node already in the recursion stack.

Heaps

42. How do you implement a priority queue using a heap?

A priority queue stores elements based on priority rather than insertion order. The most common way to implement it is to use a heap, which ensures efficient access to the highest- or lowest-priority element.

In most languages, this structure is already provided. For example, in Java, the PriorityQueue class uses a min-heap by default, where the smallest element is always at the front.

Internally, the heap maintains its order during insertion and removal operations to keep access efficient.

43. How can you find the K-th smallest element in a matrix using a min-heap?

If the matrix is sorted row-wise and column-wise:

• Add the first element of each row to a min-heap.
• Pop the smallest and push the next element in the same row.
• Repeat this K times — the K-th popped element is the answer.

class Cell {
int val, row, col;
Cell(int val, int row, int col) {
this.val = val; this.row = row; this.col = col;
}
}
int kthSmallest(int[][] matrix, int k) {
int n = matrix.length;
PriorityQueue<Cell> minHeap = new PriorityQueue<>(Comparator.comparingInt(a -> a.val));
for (int i = 0; i < n; i++) minHeap.offer(new Cell(matrix[i][0], i, 0));
for (int i = 0; i < k - 1; i++) {
Cell cell = minHeap.poll();
if (cell.col < n - 1) {
minHeap.offer(new Cell(matrix[cell.row][cell.col + 1], cell.row, cell.col + 1));
}
}
return minHeap.peek().val;
}

44. What is the process of sorting an array using a heap (heap sort)?

The process of sorting an array using a heap to sort elements:

• Build a min-heap (for ascending order).
• Then, repeatedly extract the smallest element and store it in the array.

void heapSort(int[] arr) {
PriorityQueue<Integer> heap = new PriorityQueue<>();
for (int num : arr) heap.offer(num);  // Build min-heap
for (int i = 0; i < arr.length; i++) arr[i] = heap.poll();  // Extract min
}

For in-place heap sort:

• Build a max-heap manually (using array indexing),
• Then, repeatedly swap the root with the last element and reduce the heap size.

Algorithm-based Coding Interview Questions and Answers

Algorithms provide a structured approach to finding refined solutions, solving complex problems, and proving logical thinking. They assist developers with efficient problem-solving abilities in programming interviews. Explore various algorithm-based questions focusing on sorting, searching, dynamic programming, and recursion, providing you with the skills to solve problems that test your analytical thinking.

Sorting

45. How do you implement quicksort and explain its time complexity?

On average, the time complexity to implement quicksort is O(n log n). But in the worst case, if the pivot is always the smallest or largest element, it can be O(n²) time.

void quickSort(int[] arr, int low, int high) {
if (low < high) {
int pivotIndex = partition(arr, low, high);
quickSort(arr, low, pivotIndex - 1);
quickSort(arr, pivotIndex + 1, high);
}
}
int partition(int[] arr, int low, int high) {
int pivot = arr[high];
int i = low - 1;
for (int j = low; j < high; j++) {
if (arr[j] < pivot) {
i++;
int temp = arr[i]; arr[i] = arr[j]; arr[j] = temp;
}
}
int temp = arr[i + 1]; arr[i + 1] = arr[high]; arr[high] = temp;
return i + 1;
}

Also Read: Time and Space Complexity in Data Structures

46. How is mergesort implemented, and how does it compare with quicksort?

47. How can you sort an array of strings based on their length?

To sort from shortest to longest:

Arrays.sort(arr, (a, b) -> a.length() - b.length());

Searching

48. How do you perform binary search on a sorted array?

Check the middle element to find a target in a sorted array:

• If it is the target, you're done.
• If the target is smaller, search the left half.
• If it is bigger, search the right half.
• Keep repeating until you find it or the range is empty.

int binarySearch(int[] arr, int target) {
int low = 0, high = arr.length - 1;
while (low <= high) {
int mid = low + (high - low) / 2;
if (arr[mid] == target) return mid;
else if (arr[mid] < target) low = mid + 1;
else high = mid - 1;
}
return -1; // Not found
}

49. How do you search for a target element in a rotated sorted array?

A rotated sorted array is like a sorted array that has been shifted. Use a modified binary search to check which half is sorted and decide where to search next.

50. How can you search for an element in a 2D matrix, sorted row and column-wise?

You can follow the steps:

• Start from the top-right corner.
• If the current value is bigger than the target, move left.
• If smaller, move down.
• Keep going until you find it, or go out of bounds.

Recursion

51. How do you calculate the factorial of a number using recursion?

Multiply the number by the factorial of the number just before it until it reaches the base case (1 or 0). Then, call the same function with a smaller number each time until reaching the base case.

int factorial(int n) {
if (n == 0 || n == 1) return 1;
return n * factorial(n - 1);
}

52. How would you solve the N-th Fibonacci number using recursion?

The function for (n-1) and (n-2) should be called to solve the Nth Fibonacci number using recursion.

int fibonacci(int n) {
if (n <= 1) return n;
return fibonacci(n - 1) + fibonacci(n - 2);
}

This approach is simple but not efficient for large n.

53. How do you generate all permutations of a string using recursion?

We can generate all permutations of a string using recursion to swap each character and move forward step by step.

void permute(String str, int l, int r) {
if (l == r)
System.out.println(str);
else {
for (int i = l; i <= r; i++) {
str = swap(str, l, i);
permute(str, l + 1, r);
str = swap(str, l, i); // backtrack
}
}
}
String swap(String s, int i, int j) {
char[] ch = s.toCharArray();
char temp = ch[i];
ch[i] = ch[j];
ch[j] = temp;
return String.valueOf(ch);
}

Dynamic Programming (DP)

54. How do you solve the 0/1 knapsack problem using dynamic programming?

In the 0/1 knapsack problem, you decide whether to include each item in a bag with limited capacity to maximize total value. With dynamic programming:

• Use a 2D table where rows = items and columns = weights
• Fill it using previous solutions

int knapsack(int[] weights, int[] values, int W) {
int n = values.length;
int[][] dp = new int[n + 1][W + 1];
for (int i = 1; i <= n; i++) {
for (int w = 1; w <= W; w++) {
if (weights[i - 1] <= w)
dp[i][w] = Math.max(dp[i - 1][w], values[i - 1] + dp[i - 1][w - weights[i - 1]]);
else
dp[i][w] = dp[i - 1][w];
}
}
return dp[n][W];
}

55. How can you find the longest common subsequence (LCS) of two strings?

We can use a 2D array to store the lengths of the LCS for substrings of both strings.

int lcs(String s1, String s2) {
int m = s1.length(), n = s2.length();
int[][] dp = new int[m + 1][n + 1]; 
for (int i = 1; i <= m; i++) {
for (int j = 1; j <= n; j++) {
if (s1.charAt(i - 1) == s2.charAt(j - 1))
dp[i][j] = 1 + dp[i - 1][j - 1];
else
dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]);
}
}
return dp[m][n];
}

56. How do you solve the coin change problem with dynamic programming?

Dynamic programming solves the coin change problem by finding the minimum number of coins using a 1D array. Each index represents the required coins.

int coinChange(int[] coins, int amount) {
int[] dp = new int[amount + 1];
Arrays.fill(dp, amount + 1);
dp[0] = 0;
for (int coin : coins) {
for (int i = coin; i <= amount; i++) {
dp[i] = Math.min(dp[i], dp[i - coin] + 1);
}
}
return dp[amount] > amount ? -1 : dp[amount];
}

Backtracking

57. How do you solve the N-Queens problem using backtracking?

The N-Queens problem is about putting N queens on an N×N chessboard so that none of them can attack each other. Backtracking solves it by placing one queen in a row at a time. Before putting a queen down, it checks if it would clash with any queens already on the board, either in the same column or along the diagonals. If there’s a conflict, it goes back and tries a different spot. Step by step, it goes through all possible setups until it finds a solution.

58. How would you generate all possible subsets of a set (Power Set)?

To find all subsets (power set), backtrack through each element. We either include it or skip it, forming a tree of choices.

59. How can you solve the Sudoku puzzle using backtracking?

Backtracking tries a number, moves forward, and backtracks if the placement is invalid. Thus, it ensures that digits 1-9 are placed in empty cells so that each row, column, and 3x3 box has no repeats.

System Design-based Coding Interview Questions and Answers

System design interviews assess your ability to create scalable and reliable systems. This section offers questions and answers exploring key concepts like database design, scalability, and fault tolerance, helping you develop a structured approach to designing large-scale systems.

System Design Basics

60. How would you design a URL shortening service like Bit.ly?

We need a system that generates unique short URLs for long URLs to design a URL shortening service. We can use a hash function to create a short code for the original URL and store it in a database.

• Generate unique ID: A base62 encoding (characters 0-9, a-z, A-Z) for the short URL.
• Store URL mapping: Store the original URL and the generated short code in the database.
• Redirect: When users visit the shortened URL, redirect them to the original URL.

61. How can you design a system to handle millions of concurrent requests?

To handle millions of requests, we have to build a scalable, distributed system using:

• Load Balancing: Distribute traffic across servers
• Caching (Redis/CDN): Reduce repetitive work
• Horizontal Scaling: Add more servers
• Asynchronous Processing: Use queues (like Kafka or RabbitMQ)
• Database Sharding & Replication: Improve performance
• Efficient APIs & Rate Limiting: Avoid overuse

62. How do you design a distributed file system like Google Drive?

A distributed file system stores files across multiple machines to ensure redundancy, scalability, and high availability. Follow the steps below to design a distributed file system like Google Drive:

• Split files into small chunks (e.g., 64MB per chunk).
• Store each chunk on a different node with replication for fault tolerance.
• Use a master node to keep metadata and track chunk locations.
• Provide APIs for uploading, downloading, and sharing files.

Database Design

63. How would you design a library management system?

A library management system stores information about books, members, and transactions. Here’s how you can design a library management system.

CREATE TABLE books (
book_id INT PRIMARY KEY,
title VARCHAR(100),
author VARCHAR(100),
isbn VARCHAR(20),
availability BOOLEAN
);
CREATE TABLE members (
member_id INT PRIMARY KEY,
name VARCHAR(100),
contact VARCHAR(15),
membership_status VARCHAR(20)
);
CREATE TABLE transactions (
transaction_id INT PRIMARY KEY,
book_id INT,
member_id INT,
issue_date DATE,
return_date DATE,
fine DECIMAL(5, 2),
FOREIGN KEY(book_id) REFERENCES books(book_id),
FOREIGN KEY(member_id) REFERENCES members(member_id)
);

64. How do you design a movie ticket booking system?

A movie ticket booking system stores movie details, show timings, bookings, and payment information. Here’s how you can create a movie ticket booking system.

CREATE TABLE movies (
movie_id INT PRIMARY KEY,
title VARCHAR(100),
genre VARCHAR(50),
duration INT
);
CREATE TABLE showings (
show_id INT PRIMARY KEY,
movie_id INT,
show_time DATETIME,
theater VARCHAR(100),
FOREIGN KEY(movie_id) REFERENCES movies(movie_id)
);
CREATE TABLE bookings (
booking_id INT PRIMARY KEY,
user_id INT,
show_id INT,
seats INT,
booking_time DATETIME,
FOREIGN KEY(show_id) REFERENCES showings(show_id)
);

65. How would you design the backend of an e-commerce platform (products, orders, users)?

The backend for an e-commerce platform consists of entities like Users, Products, Orders, and Payments. These are connected by relationships like "User can place multiple orders" or "Order contains multiple products."

CREATE TABLE users (
user_id INT PRIMARY KEY,
username VARCHAR(50),
password VARCHAR(100),
email VARCHAR(100)
);
CREATE TABLE products (
product_id INT PRIMARY KEY,
name VARCHAR(100),
price DECIMAL(10, 2),
description TEXT,
stock INT
);
CREATE TABLE orders (
order_id INT PRIMARY KEY,
user_id INT,
order_date DATETIME,
total_amount DECIMAL(10, 2),
status VARCHAR(50),
FOREIGN KEY(user_id) REFERENCES users(user_id)
);
CREATE TABLE order_items (
order_item_id INT PRIMARY KEY,
order_id INT,
product_id INT,
quantity INT,
FOREIGN KEY(order_id) REFERENCES orders(order_id),
FOREIGN KEY(product_id) REFERENCES products(product_id)
);

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Scalability

66. How would you design a load-balancing system for an e-commerce website?

To design a load balancing system for an e-commerce website,

• use multiple load balancers to distribute traffic evenly across web servers,
• and ensure horizontal scaling by adding more servers during high traffic periods.

A round-robin or least-connections method can be used for routing requests. Additionally, session persistence (sticky sessions) can ensure that users are consistently directed to the same server during their session.

Health checks on the servers should be implemented to route traffic only to healthy servers.

67. How would you handle real-time stock price data in a scalable manner?

To handle real-time stock price data, implement a message queue system like Kafka to process high volumes of incoming stock price updates in real time.

The data can then be broadcast to clients through WebSockets or similar technologies to push the updates instantly. To ensure scalability, horizontally scale the message processing and storage systems.

A distributed caching layer, like Redis, could store frequently accessed data to reduce database load and improve response time.

68. How would you design a system for high-frequency trading?

Low latency is critical for high-frequency trading, so focus on designing a system that minimizes network hops and ensures fast execution. This can be achieved through co-located servers near the stock exchanges and direct market access.

Use highly optimized, low-latency programming languages like C++ or Rust, and implement in-memory data stores for quick access to market data. Efficient decision-making, risk management algorithms, and high-speed networking protocols like RDMA would ensure quick order execution.

Fault Tolerance and Reliability

69. How would you design a fault-tolerant database system?

To design a fault-tolerant database system, replication is used to ensure data availability in case of a failure. Master-slave or multi-master replication can be set up to distribute data across multiple nodes. Data consistency could be managed using techniques like eventual consistency for high availability.

A backup and recovery plan should be in place to restore data from a recent snapshot in case of catastrophic failure. Regular monitoring and automated failover mechanisms would ensure minimal downtime during failures.

Q70. How would you handle consistency and availability based on the CAP theorem?

The CAP theorem states that a distributed system can have only two of the following three at the same time: consistency, availability, and tolerance for network splits. In real life, network issues happen, so systems have to pick between consistency and availability. For example, banking systems usually prioritize consistency, ensuring all transactions are correct. At the same time, social media apps often prioritize availability, so feeds stay up even if some data isn’t fully synced.

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Additional Coding Interview Questions

1. How should you think while solving coding interview questions?

In coding interviews, interviewers care as much about your thinking as your final answer. A good approach starts with clearly understanding the problem, asking about constraints, and identifying edge cases. Once the problem is clear, choosing the proper data structure becomes easier.

After that, explain your logic step by step before writing code. Start with a simple solution, then improve it by reducing time or space complexity if needed. Clear communication helps the interviewer follow your reasoning and shows confidence, even if the solution is not perfect.

2. Why is time complexity essential in coding interviews?

Time complexity basically tells you how your code will behave as the input gets bigger. In interviews, you’re often asked to compare different solutions and explain why one is faster or better than another.

Interviewers expect you to know Big O notation, like O(1), O(log n), O(n), O(n log n), and O(n²). Understanding how loops, recursion, and nested operations affect performance helps you write code that’s not just correct but also efficient.

3. What is the sliding window technique in coding interviews?

The sliding window technique comes in handy when you’re dealing with subarrays or substrings. Instead of recalculating everything every time, you move a “window” across the data and update your results as you go.

People often use this method to find the maximum sum of a subarray of size K, the longest substring without repeating characters, or the smallest substring that contains all required characters. It usually brings down the time from O(n²) to O(n), which is why it’s a favorite in coding interviews.

4. What is the difference between a coding interview and a system design interview?

A coding interview is all about solving problems with code. You’ll work with data structures and algorithms, write solutions, and explain how efficient your code is.

A system design interview looks at the bigger picture. Instead of writing code, you plan how a system should work, handle lots of users, and deal with real-world challenges. It’s more about architecture, scaling, and trade-offs. Both test your problem-solving skills, but in very different ways.

5. What are the best resources for coding interview preparation?

The best way to prepare is to practice coding problems regularly while keeping your basics strong. Solving questions of different difficulty levels helps improve both thinking speed and confidence. At the same time, revisiting topics such as data structures, algorithms, and time complexity makes problem-solving much easier in interviews.

Mock interviews and structured courses can also help, especially to understand how real interviews are conducted. Learning from explanations, reviewing mistakes, and practicing consistently usually works better than relying on just one method.

What Developers Share About Coding Interviews on Reddit?

Practicing coding problems definitely helps, but hearing from people who have already faced interviews adds a lot of real value. On Reddit, there is a well-known discussion called “a misunderstanding of the coding interview,” where developers talk openly about what actually happens in interviews. Many point out that candidates often worry too much about writing the perfect solution and forget what interviewers are really watching for.

In that discussion, several developers explain that interviewers care more about how you approach a problem. They notice how you explain your thoughts, respond to hints, and deal with follow-up questions. Some users even share cases where their code was not fully optimized, but they still did well because they communicated clearly. Reading experiences like these can change how you prepare and help you focus on what matters during an interview.

Watch the video below, which deals with real-time and corporate-level coding-based interview questions and answers.

Tips to Prepare for a Coding Interview

Now, you can access the top 70 coding interview questions and answers. Let us have a look at the key tips to prepare for your coding interview:

• Learn Data Structures and Algorithms: Focus on common coding topics often asked in coding interviews
• Practice Coding Problems Daily: Use coding websites to solve problems regularly. Start easy and slowly move to medium, and then practice hard levels
• Choose One Programming Language and Master It: A single language at a time is easy to master. You can just stick to one language and get very comfortable using it for problem-solving
• Do Mock Interviews: Practice with friends, mentors, or online platforms. It helps improve your thinking speed and confidence under pressure
• Explain Your Thinking Out Loud: During interviews, clearly explain how you approach the problem. Interviewers want to understand your logic, not just see the final answer
• Assess Common Coding Questions: Study the most common questions about coding. You can read coding books, too
• Understand the Job Role and Company: Carefully read the job description and research the company. This way, you will know where to focus and prepare better for technical rounds

Key Takeaways

• In coding interviews, it’s not just about getting the correct answer. Interviewers want to see how you think and how you approach problems, so explain your logic step by step
• Make sure you’re comfortable with data structures, algorithms, and common patterns like arrays, recursion, and dynamic programming
• Try practicing questions of all difficulty levels. This way, you’ll feel ready for both the easy and the tricky problems in a real interview
• Having a plan helps a lot. Include coding practice, checking how efficient your solutions are, and doing mock interviews. It builds confidence and makes you more prepared for the real thing

FAQs

1. What is a coding interview and what do companies test?

A coding interview checks how you solve problems, write logical code, and explain your thinking. Companies mainly assess problem-solving ability, coding fundamentals, and clarity of approach.

2. How do I prepare for a coding interview step by step?

Start with basics, practice data structures and algorithms, solve problems daily, analyze time complexity, and regularly explain your solutions out loud while coding.

3. What are the most common coding interview questions?

Most coding questions for an interview include problems on arrays, strings, linked lists, recursion, searching, sorting, and basic algorithm design.

4. How many DSA problems should I solve before interviews?

There is no fixed number, but solving 150–300 well-chosen problems across topics is usually enough to build confidence and pattern recognition.

5. What topics are most important (arrays, strings, trees, DP)?

If you’re starting out, focus on arrays and strings first. After that, get comfortable with linked lists, stacks, and queues. Once you’re confident with those, move on to trees, recursion, and dynamic programming, which are usually needed for more advanced roles.

6. How do I improve problem-solving speed in interviews?

The best way is to practice regularly so you start spotting patterns quickly. Try to avoid brute-force approaches unless the problem asks explicitly for them. Over time, this helps you think and act faster under pressure.

7. How do I explain my approach clearly while coding?

Speak through your logic before writing code, explain edge cases, and describe why a solution works rather than just writing syntax.

8. What are common mistakes to avoid in coding interviews?

Skipping edge cases, failing to clarify the problem, rushing into code, and ignoring time or space complexity are common mistakes.

9. How do I handle edge cases and constraints?

Always think about empty inputs, minimum and maximum values, duplicates, and constraints before finalizing your solution.

10. What is time and space complexity, and how to analyze it?

Time complexity tells you how long your code will take as the input gets bigger. Space complexity is about how much memory your code uses. To figure them out, look at your loops, recursion, and any extra stuff your program stores.

11. What is the best language for coding interviews (Python/Java/C++)?

Choose the language you are most comfortable with. Python is concise, Java is structured, and C++ offers performance control. Logic matters more than syntax.

12. What should freshers focus on for coding interviews?

Freshers should master the fundamentals, practice basic coding questions for interviews, and focus on writing clean, understandable code.

13. How do I prepare for FAANG-style coding interviews?

Focus on problem patterns, optimize solutions, practice mock interviews, and regularly solve high-quality interview coding questions.

14. What should I do if I get stuck during an interview?

Pause, restate the problem, ask clarifying questions, and explain partial logic instead of staying silent.

15. How are coding interviews different in India vs the USA?

The types of coding questions are mostly the same, but the approach is different. In the US, interviewers usually want you to explain your thought process and discuss your solution. In India, they often focus more on solving the problem quickly and getting the answer right.