Top 25 Coding Interview Questions You Should Know About
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- Jaro Education
- 22, February 2025
- 11:00 am
Preparing for coding interviews is crucial for landing your dream job in tech. These interviews are designed to assess your problem-solving skills, logical thinking, and ability to write clean, efficient code. Mastering common basic coding interview questions can significantly increase your chances of acing the interview, as they often test concepts that come up repeatedly across various companies. Whether you’re preparing for your first technical interview or refreshing your knowledge of programming interview questions, understanding these core problems can give you the edge you need.
What to Expect in a Coding Interview
When you’re entering a coding interview, it’s important to know what to expect. Typically, the interview will be divided into multiple stages, including an initial screening (often by phone or video), followed by live coding problems, and sometimes even a system design interview. During the coding phase, you’ll likely be asked to solve a problem or series of problems within a set time frame. These problems usually test your knowledge of data structures, algorithms, and general problem-solving abilities. You may also be asked to optimize your solutions for efficiency.
The main topics you’ll encounter include:
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- Data Structures: Arrays, linked lists, stacks, queues, trees, and graphs.
- Algorithms: Searching, sorting, dynamic programming, and recursion.
- Problem-Solving: Logical reasoning, pattern recognition, and approaching new problems.
Why These 25 Coding Interview Questions?
These 25 basic coding interview questions are selected because they cover the most common and important concepts that tech companies look for in candidates. From data structures to algorithms, these problems address the core skills that hiring managers evaluate during interviews. By preparing for these coding interview questions, you’ll familiarize yourself with the types of problems you can expect and gain confidence in your ability to tackle them.
These Java coding interview questions are designed to challenge your understanding and help you demonstrate your proficiency in coding, critical thinking, and problem-solving under pressure. Solving these problems will give you the foundational knowledge to handle most coding interview scenarios.
Top 25 Coding Interview Questions
Data Structures Coding Interview Questions
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1. Reverse a Linked List
This classic problem asks you to reverse a singly linked list. The expected approach is to iterate through the list while changing the direction of the pointers. You’ll need to use a few pointers: one for the current node, one for the previous node, and one for the next node to avoid losing track of the list as you reverse it.
2. Find the Middle Element of a Linked List
To find the middle element of a linked list, there are a few ways to solve the problem. A simple approach is to use two pointers: one moves one node at a time (slow pointer), and the other moves two nodes at a time (fast pointer). When the fast pointer reaches the end, the slow pointer will be at the middle element. If the list has an even number of elements, return the second middle element.
3. Implement a Stack Using Queues
In this problem, you’re tasked with implementing a stack using two queues. The basic idea is to use the queues for different operations in the stack: one queue holds the elements, and the other helps simulate the stack’s Last-In-First-Out (LIFO) behavior. You can perform push and pop operations by utilizing the two queues cleverly, with enqueue and dequeue operations mimicking the stack behavior.
4. Check if a Linked List is Palindrome
To check if a linked list is a palindrome, you need to compare the first half of the list with the second half in reverse order. One approach is to use the slow-fast pointer technique to find the middle of the list and then reverse the second half. Finally, you compare the first half and the reversed second half element by element.
5. Find the Nth Node from the End of a Linked List
To find the Nth node from the end of a linked list, the optimal solution uses the slow and fast pointer technique. Move the fast pointer N nodes ahead, then move both pointers one node at a time. When the fast pointer reaches the end of the list, the slow pointer will be at the Nth node from the end.
Algorithms Coding Interview Questions
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6. Binary Search
Binary search is a fast way to find a target element in a sorted array by repeatedly dividing the search interval in half. The time complexity is O(log n) because the search space is halved with each step. Apply binary search when you need to search in sorted arrays or lists.
7. Merge Sort
Merge sort is a divide-and-conquer algorithm that splits the array into halves, recursively sorts them, and then merges the sorted halves. It runs in O(n log n) time, making it one of the most efficient sorting algorithms, especially when working with large datasets.
8. Quick Sort
Quick sort also uses a divide-and-conquer approach but works by selecting a “pivot” element and partitioning the array around the pivot. The time complexity for quick sort is O(n log n) on average, but in the worst case, it can be O(n^2). Compared to merge sort, quick sort generally performs better in practice due to its smaller constant factors.
9. Find the Largest Subarray Sum
This problem can be solved using Kadane’s algorithm, which efficiently finds the maximum sum of a contiguous subarray. The algorithm iterates through the array, keeping track of the current subarray sum and updating the maximum sum found so far. It works in O(n) time complexity.
10. Find the Missing Number in an Array
Given an array of n-1 integers from a range 1 to n, this problem asks you to find the missing number. A simple and optimal solution uses the sum formula for the first n numbers. Subtract the sum of the array elements from the expected sum, and the result is the missing number. The time complexity is O(n), and the space complexity is O(1).
Arrays and Strings Coding Interview Questions
1. Two SumÂ
In this problem, you need to find two numbers in an array that add up to a specific target. The most efficient solution uses a hash map to store the numbers you’ve seen so far, checking if the complement of the current number (target – current number) is in the hash map. This approach runs in O(n) time.
2. Longest Substring Without Repeating Characters
To solve this, you can use the sliding window technique. Use two pointers to maintain a window of characters without duplicates. Expand the window by moving the right pointer and contract it by moving the left pointer when a duplicate is found. The time complexity is O(n) because each character is processed at most twice.
3. Rotate an Array
Array rotation involves shifting all elements of an array by a certain number of positions. The optimal way to do this is by reversing parts of the array. First, reverse the entire array, then reverse the first k elements and the remaining n-k elements. This solution works in O(n) time.
4. String Compression
This problem asks you to compress a string by counting consecutive occurrences of characters. For example, “aabcccccaaa” should be compressed to “a2b1c5a3”. A simple approach involves iterating through the string and counting consecutive characters. The time complexity is O(n), where n is the length of the string.
5. Anagram Check
To check if two strings are anagrams, you can either sort both strings and compare them, or use a hash map to count the frequency of each character in both strings. If the frequency counts are identical, the strings are anagrams. This approach works in O(n log n) time (for sorting) or O(n) time (for hash maps).
Dynamic Programming Coding Interview Questions
1. Fibonacci Sequence
The Fibonacci sequence can be solved both iteratively and recursively. In the recursive approach, the function calls itself for the two previous numbers in the sequence, leading to an exponential time complexity of O(2^n). This can be inefficient for large numbers. On the other hand, the iterative solution is more efficient, running in O(n) time. It uses two variables to store the current and previous Fibonacci numbers, updating them as you iterate through the sequence.
2. Longest Common Subsequence (LCS)Â
The Longest Common Subsequence problem involves finding the longest subsequence that is common between two sequences. This can be solved using dynamic programming. You create a 2D table where each cell represents the LCS length for the substrings of the two sequences. By filling in the table iteratively, you can find the solution in O(m * n) time, where m and n are the lengths of the two sequences. The solution involves comparing characters and making decisions based on previous results in the table.
3. Coin Change Problem
The coin change problem asks how to make a certain amount using the fewest number of coins from a given set. This is a classic dynamic programming problem. The key idea is to build up a solution for all values from 0 to the target amount by considering each coin type at each step. You create an array where each index represents the minimum number of coins needed to make that value. The time complexity of this approach is O(n * m), where n is the target amount and m is the number of coin types.
Trees and Graphs Coding Interview Questions
1. Binary Tree Inorder Traversal
Inorder traversal is a way of visiting all the nodes in a binary tree where you visit the left subtree first, then the current node, and finally the right subtree. There are two ways to perform this traversal:
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- Recursive approach: Recursively visit the left subtree, then the current node, and then the right subtree.
- Iterative approach: Use a stack to simulate the recursive calls. Push nodes onto the stack as you traverse left, and when you reach a leaf node, pop the stack to visit the current node.
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- Breadth-first search (BFS) explores all neighbors at the present depth before moving on to nodes at the next depth level. It’s typically used for finding the shortest path in an unweighted graph. BFS uses a queue for the exploration process and has a time complexity of O(V + E), where V is the number of vertices and E is the number of edges.
- Depth-first search (DFS) explores as far as possible along each branch before backtracking. DFS uses a stack (or recursion) and can be used to explore all possible paths in a graph. Its time complexity is also O(V + E), but it may use more memory than BFS in cases where the graph is deep.
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- DFS: As you traverse the graph, if you encounter a node that is already in the current path, a cycle exists. You maintain a visited set to keep track of nodes and their states.
- Union-Find: This technique works by keeping track of connected components. If two nodes that are about to be connected are already in the same component, a cycle exists. The time complexity is O(V + E) using DFS or O(α(V)) with Union-Find, where α is the inverse Ackermann function, which is almost constant.
How to Prepare for Coding Interviews
To effectively prepare for coding interviews, it’s important to practice consistently and approach each of these coding interview questions methodically. Here are some tips to help you prepare:
- Practice Regularly: Set aside dedicated time each day to solve coding problems. Platforms like  , HackerRank, and CodeSignal offer a wide range of problems at varying difficulty levels, allowing you to build up your skills.
- Understand the Problem: Before diving into the solution, take a moment to fully understand the problem. Break it down into smaller pieces and think about the constraints and edge cases. This will help you avoid rushing into a solution that may not work in all cases.
- Time Management: During the interview, manage your time wisely. Aim to come up with a solution within the allotted time, but don’t rush through it. Focus on delivering a working solution first and optimize it only after you have the correct logic.
- Optimize the Solution: While solving problems, try to focus not only on finding a solution but also on making it optimal. Think about the time and space complexity of your solution and look for ways to improve it.
Final Thoughts
The key to excelling in coding interviews is consistent practice and understanding of the core concepts. By solving a variety of problems across different topics, you’ll familiarize yourself with common patterns and strategies that can be applied in real interviews.
It’s also essential to focus on problem-solving techniques and optimize your solutions. Remember, interviewers are not just looking for correct answers, but also how you approach solving the problem and improving your solutions.
So, keep practicing these coding interview questions, stay persistent, and keep challenging yourself with new coding interview questions. Take advantage of online resources and interview preparation tools to fine-tune your skills and track your progress. The more you practice coding interview questions, the more confident you’ll become in your ability to tackle coding interviews successfully.
Good luck, and happy coding!
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