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Top 40 Coding Interview Questions & Answers for 2025

If you’re preparing for coding interviews in 2025, you’ve come to the right place! The competition for software development and engineering roles is fierce, and being ready with the right knowledge can make all the difference. This guide covers the Top 40 Coding Interview Questions & Answers that will help you crack technical interviews in 2025.

These questions cover data structures, algorithms, object-oriented programming, system design, and problem-solving techniques. Whether you’re a fresher or an experienced developer, these coding questions and answers will sharpen your skills and give you the confidence to ace your next interview.

Why Prepare for Coding Interviews in 2025?

Technology evolves rapidly. Recruiters are now looking for candidates who not only know the basics but also understand the latest trends in data structures, algorithms, and efficient coding practices. Preparing for coding interviews in 2025 means staying ahead with optimized solutions, best practices, and hands-on problem-solving.

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For more tips on preparing for technical interviews, visit our Coding Interview Preparation Guide.

Top 40 Coding Interview Questions & Answers (2025)

1. What is Big O Notation? Explain with examples.

Answer:
Big O notation describes the time complexity or space complexity of an algorithm in terms of input size n. It provides an upper bound on the growth rate of an algorithm.

Example:

• O(1): Constant time. Example: accessing an element by index in an array.
• O(n): Linear time. Example: traversing an array.
• O(log n): Logarithmic time. Example: binary search.

For a detailed explanation of Big O notation, check out this Big O Notation guide on GeeksforGeeks.

2. Explain the difference between Array and Linked List.

Answer:

For more details on Arrays and Linked Lists, read this comprehensive article on Linked List vs Arrays on Programiz.

3. How does HashMap work in Java?

Answer:
HashMap stores data in key-value pairs. It uses a hash function to compute an index (hashcode), and keys with the same hash are stored in a linked list or balanced tree (since Java 8).
Lookup, insertion, and deletion are generally O(1) but can degrade to O(n) in worst-case collisions.

You can read more about how HashMap works in Java at Oracle’s official Java documentation.

4. Reverse a Linked List.

Answer (Iterative Approach):

javaCopyEditpublic ListNode reverseList(ListNode head) {
    ListNode prev = null;
    while (head != null) {
        ListNode next = head.next;
        head.next = prev;
        prev = head;
        head = next;
    }
    return prev;
}

Time Complexity: O(n)
Space Complexity: O(1)

Check out this Linked List tutorial on GeeksforGeeks for a deeper understanding of Linked List operations.

5. Detect a cycle in a Linked List.

Answer:
Use Floyd’s Cycle Detection Algorithm (Tortoise and Hare).

javaCopyEditpublic boolean hasCycle(ListNode head) {
    ListNode slow = head;
    ListNode fast = head;
    while (fast != null && fast.next != null) {
        slow = slow.next;
        fast = fast.next.next;
        if (slow == fast)
            return true;
    }
    return false;
}

Floyd’s cycle detection is explained in-depth in this tutorial by Programiz.

6. What is a Binary Search Tree (BST)?

Answer:
A BST is a binary tree where:

• Left child < Parent
• Right child > Parent

Efficient for search, insert, and delete operations (O(log n) on average).

For more details on Binary Search Trees, visit this article on GeeksforGeeks.

7. Find the height of a binary tree.

Answer (Recursive Approach):

javaCopyEditpublic int height(TreeNode root) {
    if (root == null) return 0;
    return 1 + Math.max(height(root.left), height(root.right));
}

Time Complexity: O(n)

For more on tree traversal techniques, check this GeeksforGeeks article.

8. What is Dynamic Programming (DP)?

Answer:
DP solves complex problems by breaking them into simpler subproblems, storing the results to avoid redundant calculations.
Two approaches:

• Top-down (Memoization)
• Bottom-up (Tabulation)

A fantastic resource for Dynamic Programming problems and solutions is this article on GeeksforGeeks.

9. Fibonacci Sequence using Dynamic Programming.

Answer (Bottom-Up Tabulation):

javaCopyEditpublic int fib(int n) {
    if (n <= 1) return n;
    int[] dp = new int[n + 1];
    dp[0] = 0; dp[1] = 1;
    for (int i = 2; i <= n; i++) {
        dp[i] = dp[i - 1] + dp[i - 2];
    }
    return dp[n];
}

For a thorough explanation on solving Fibonacci using DP, check out this Programiz article.

10. Explain Time Complexity of Merge Sort.

Answer:

• Time Complexity: O(n log n)
• Space Complexity: O(n)
  Merge Sort divides the array into halves, sorts them recursively, and merges them.

To understand Merge Sort more clearly, check out this article on Merge Sort at GeeksforGeeks.

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For additional coding challenges and interview resources, check out our Coding Problem Solvers Hub.

Conclusion for Top 40 Coding Interview Questions

These Top 40 Coding Interview Questions and Answers for 2025 will prepare you for any technical interview, whether it’s for a FAANG company or a start-up. Master these concepts, write clean and optimized code, and practice consistently. Don’t just memorize answers—understand the underlying principles, and you’ll stand out in any Top 40 Coding Interview Questions

Ready to land your dream job?
Start practicing these problems today and ace your coding interviews in 2025!