Top 8 Coding Interview Questions Every Hiring Manager Should Ask

coding interview questions


Coding interviews have become an important part of the hiring process in the competitive software development landscape. With the help of coding interviews, hiring managers can evaluate technical skills and gauge candidates’ problem-solving abilities, adaptability, and even communication skills. 

Given developers’ critical role in driving innovation and operational success, ensuring that candidates possess the right skills is important. This is where coding interviews come into play, providing a structured framework to assess potential hires effectively.

According to, the average pass rate for technical interviews is around 54%, reportedly as low as 15 to 20 percent at tech giants that have highly competitive and comprehensive hiring processes. This statistic highlights the challenge of finding qualified candidates and shows the importance of designing effective and revealing coding interviews.

coding interview questions

In this article, we will explore the top 8 coding interview questions every hiring manager should ask. These questions cover various topics, including language-specific problems, algorithms, data structures, and array manipulations. 

Top 8 Coding Interview Questions

Let’s explore the top 8 coding interview questions:

1. Java Language-Specific Problem: Implement a Singleton Pattern

Task Implement the Singleton design pattern in Java. The Singleton pattern ensures that a class has only one instance and provides a global point of access to that instance. Make sure your implementation is thread-safe without using the synchronized keyword on the getInstance() method.
  • Ensure the class is thread-safe.
  • Avoid using the synchronized keyword on the getInstance() method to improve performance.
  • Use lazy initialization for the singleton instance.

Suggested Answer

public class Singleton {

    private static volatile Singleton instance;

    private Singleton() {}

    public static Singleton getInstance() {

        if (instance == null) {

            synchronized (Singleton.class) {

                if (instance == null) {

                    instance = new Singleton();




        return instance;



Code Explanation

This code snippet implements the Singleton design pattern using a technique called “Double-Checked Locking” to reduce the use of synchronization. By declaring the instance variable volatile, we ensure multiple threads correctly handle the singleton instance. The Singleton’s constructor is private to prevent direct instantiation.

Common Mistakes to Watch Out For
  • Forgetting to declare the instance variable as volatile, which is crucial for thread safety in this context.
  • Implementing the Singleton without considering thread safety, leading to potential issues in a multi-threaded environment.
  • Using eager initialization unnecessarily can lead to resource wastage if the instance is never used.
  • Discuss how different initialization techniques (lazy vs. eager) affect the performance and resource utilization of your application.
  • How would you implement the Singleton pattern using an enum type in Java?
What the question tests This question assesses a candidate’s understanding of Java’s concurrency features, design patterns (specifically Singleton), and their ability to write clean, efficient, and thread-safe code. It also tests knowledge of Java memory model nuances (volatile keyword usage) and the ability to apply design patterns in a practical scenario.

2. Python Language-Specific Problem: Decorators for Function Execution Time Logging

Task Write a Python decorator that logs the execution time of any function it decorates. The decorator should print the function’s name and how long it took to execute in seconds.
  • The decorator should be flexible enough to be applied to any function.
  • Ensure the timing is accurate to the nearest millisecond.
Output Format Print statements that show the function’s name and its execution time in seconds.
Sample Input Assume the decorator is applied to a function sample_function that takes no arguments and sleeps for 2 seconds.
Sample Output sample_function executed in 2.0023 seconds.

Suggested Answer

import time

from functools import wraps

def execution_time_logger(func):


    def wrapper(*args, **kwargs):

        start_time = time.time()

        result = func(*args, **kwargs)

        end_time = time.time()

        print(f”{func.__name__} executed in {end_time – start_time:.4f} seconds.”)

        return result

    return wrapper


def sample_function():



Code Explanation

This code defines a decorator execution_time_logger that measures and prints the execution time of the decorated function. The @wraps(func) decorator preserves the original function’s information, such as its name. The decorator calculates the time before and after the function call, computing the difference to find the execution time, which it then prints.

Common Mistakes to Watch Out For
  • Not using @wraps(func) from the functools module, which can lead to lost metadata about the original function.
  • Forgetting to return the result of the decorated function, altering the function’s expected behavior.
  • Incorrectly calculating the time, leading to inaccurate logging of the execution time.
  • How would you modify the decorator to log the execution time to a file instead of printing it?
  • Discuss how decorators in Python can be used to add other types of logging (e.g., arguments passed, return values).
What the question tests This question evaluates a candidate’s understanding of advanced Python features, such as decorators, and their ability to work with the Python standard library. It also tests the candidate’s knowledge of Python’s timing functions and their precision and the ability to enhance function behavior non-intrusively using decorators.

3. JavaScript Language-Specific Problem: Implementing Throttling

Task Write a JavaScript function that implements throttling. Throttling is a technique that limits the number of function calls that occur in a given timeframe. The throttled function should only execute the last call made within the timeframe after the timeframe has elapsed.
Input Format The input will be a function func and a number limit representing the timeframe in milliseconds.
  • Ensure the throttling function is generic and can be applied to any function.
  • The limit should accurately represent the minimum time interval between two successive calls of the throttled function.
Sample Input Applying the throttle to a function that logs the current time to the console, with a limit of 1000 milliseconds.
Sample Output The output will depend on how often the throttled function is called, but it will ensure the function is not executed more than once per 1000 milliseconds.

Suggested Answer

function throttle(func, limit) {

    let lastFunc;

    let lastRan;

    return function() {

        const context = this;

        const args = arguments;

        if (!lastRan) {

            func.apply(context, args);

            lastRan =;

        } else {


            lastFunc = setTimeout(function() {

                if (( – lastRan) >= limit) {

                    func.apply(context, args);

                    lastRan =;


            }, limit – ( – lastRan));




// Example usage

const throttledLog = throttle(() => console.log(new Date()), 1000);

setInterval(throttledLog, 100); // Attempts to log the time every 100ms but is throttled to once per 1000ms

Code Explanation

This code snippet creates a throttle function that limits the frequency at which a given function func can be executed. It uses closures to keep track of the last execution time and a timeout to manage the delay until the next allowed execution. The throttle function ensures that func is only called once per the specified limit period, even if it is attempted more frequently.

Common Mistakes to Watch Out For
  • Not clearing the previous timeout, which can lead to unexpected behavior or multiple executions in quick succession.
  • Incorrectly handling the timing, leading to more frequent execution than specified by the limit.
  • Losing the context of this or arguments passed to the function when it is called.
  • How could you modify the throttling function to ensure the first call is executed immediately and subsequent calls are throttled?
  • Discuss the differences between throttling and debouncing, and when you might use one technique over the other.
What the question tests This question assesses a candidate’s understanding of JavaScript’s event handling and asynchronous programming patterns. It tests the ability to work with closures, timing functions, and the correct application of JavaScript’s functional programming features to control the execution rate of functions, which is a common requirement in web development to improve performance and user experience.

4. Algorithms Question: Finding the Kth Largest Element in an Unsorted Array

Task Write an algorithm to find the kth largest element in an unsorted array. Do not sort the array for this task.
Input Format
  • An array of integers, arr.
  • An integer k, where k is less than or equal to the size of the array.
Output Format Return the kth largest element in the array.
Sample Input arr = [3,2,1,5,6,4]

k = 2

Sample Output 5

Suggested Answer

A solution can be implemented using a min-heap to keep track of the top k largest elements in the array. The kth largest element will be at the root of the min-heap after processing all elements.

import heapq

def findKthLargest(nums, k):

    minHeap = []

    for num in nums:

        heapq.heappush(minHeap, num)

        if len(minHeap) > k:


    return minHeap[0]

Code Explanation

This code uses a min-heap to efficiently find the kth largest element in an unsorted array. By maintaining a min-heap of size k, we ensure that the smallest element in the heap is the kth largest element in the array. This approach is more efficient than sorting the entire array, especially for large arrays or when k is small.

Common Mistakes to Watch Out For
  • Attempting to sort the array first, which leads to a less efficient solution.
  • Misunderstanding the definition of “kth largest” (e.g., confusing it with kth smallest).
  • Incorrectly managing the heap size or not properly initializing the heap.
  • How would your approach change if you needed to find the kth smallest element?
  • Can you improve the algorithm to achieve better than O(nlogk) complexity in some cases?
What the question tests This question assesses the candidate’s understanding of heap data structures and their ability to apply them to solve optimization problems. It also tests the candidate’s knowledge of algorithmic efficiency and their skill in writing clean, efficient code to solve complex problems without resorting to the most straightforward, potentially less efficient methods (like sorting the entire array).

5. Algorithms Question: Implement Binary Search on a Rotated Sorted Array

Task Given a rotated sorted array, implement a function to perform a binary search for a given target value. The array was originally sorted in increasing order, but then it was rotated at some unknown pivot. You may assume no duplicate exists in the array.
Input Format
  • An array of integers, arr, representing the rotated sorted array.
  • An integer target, the value to search for.
Output Format Return the index of target in arr if it’s present, otherwise return -1.
Sample Input arr = [4,5,6,7,0,1,2]

target = 0

Sample Output 4

Suggested Answer

def search(nums, target):

    left, right = 0, len(nums) – 1

    while left <= right:

        mid = (left + right) // 2

        if nums[mid] == target:

            return mid

        # The left half is sorted

        if nums[left] <= nums[mid]:

            if nums[left] <= target < nums[mid]:

                right = mid – 1


                left = mid + 1

        # The right half is sorted


            if nums[mid] < target <= nums[right]:

                left = mid + 1


                right = mid – 1

    return -1

Code Explanation

This code snippet implements a modified binary search algorithm to handle the rotated sorted array. It first determines which half of the array is in sorted order (either the left or right half from the midpoint) and then checks if the target is within that sorted half. Depending on the outcome, it adjusts the search range accordingly, ensuring that the search process is efficient even with the rotation.

Common Mistakes to Watch Out For
  • Failing to correctly identify the sorted portion of the array, leading to incorrect range adjustments.
  • Incorrectly handling edge cases, such as when the target is at the midpoint or when the array is not rotated.
  • Overlooking the condition where the array might not have been rotated at all.
  • How would you modify your solution if the array contained duplicates?
  • Discuss the time complexity of your solution and whether there’s any scenario where the algorithm might degrade to linear search.
What the question tests This question evaluates the candidate’s ability to adapt and apply binary search to a non-standard problem setting, specifically searching in a rotated sorted array. It tests understanding of binary search principles, decision-making based on array characteristics, and the ability to handle edge cases gracefully. The problem also highlights the candidate’s skill in devising efficient algorithms to solve complex problems.

6. Data Structure Question: Implement a Max Heap

Task Implement a Max Heap from scratch. Provide functions for insertion, deletion, and to fetch the maximum element. Do not use any built-in library or API for heap operations.
Input Format
  • A series of integers for insertion into the heap.
  • Operations indicating insertion, deletion, and fetch maximum.
Output Format For fetch maximum operation, return the maximum element in the heap. For deletion, remove the maximum element.
Sample Input Insertions: [3, 10, 5, 1, 2]

Operations: Insert 3, Insert 10, Fetch Maximum, Delete Maximum, Fetch Maximum

Sample Output Fetch Maximum: 10

Fetch Maximum after Deletion: 5

Suggested Answer

class MaxHeap:

    def __init__(self):

        self.heap = []

    def insert(self, val):


        self._bubble_up(len(self.heap) – 1)

    def getMax(self):

        return self.heap[0] if self.heap else ‘Heap is empty’

    def deleteMax(self):

        if len(self.heap) > 1:

            self._swap(0, len(self.heap) – 1)

            maxVal = self.heap.pop()


        elif self.heap:

            maxVal = self.heap.pop()


            return ‘Heap is empty’

        return maxVal

    def _bubble_up(self, index):

        parent = (index – 1) // 2

        while index > 0 and self.heap[parent] < self.heap[index]:

            self._swap(index, parent)

            index = parent

            parent = (index – 1) // 2

    def _bubble_down(self, index):

        largest = index

        left = 2 * index + 1

        right = 2 * index + 2

        if left < len(self.heap) and self.heap[left] > self.heap[largest]:

            largest = left

        if right < len(self.heap) and self.heap[right] > self.heap[largest]:

            largest = right

        if largest != index:

            self._swap(index, largest)


    def _swap(self, i, j):

        self.heap[i], self.heap[j] = self.heap[j], self.heap[i]

# Example usage

heap = MaxHeap()






print(heap.getMax())  # 10


print(heap.getMax())  # 5

Code Explanation

This code snippet demonstrates how to implement a max heap. It includes methods for insertion (insert), fetching the maximum value (getMax), and deletion of the maximum value (deleteMax). The _bubble_up and _bubble_down methods ensure the heap property is maintained after each insertion or deletion, respectively, by adjusting the positions of the elements within the heap.

Common Mistakes to Watch Out For
  • Not correctly maintaining the heap property after insertions or deletions, leading to incorrect getMax results.
  • Failing to handle edge cases, such as deleting from an empty heap or inserting values outside the allowed range.
  • How would you implement a min heap based on this structure?
  • Discuss how heap data structures can be used in sorting algorithms and their efficiency compared to other sorting methods.
What the question tests This question assesses the candidate’s understanding of heap data structures, specifically max heaps, and their ability to implement fundamental operations from scratch. It tests knowledge of dynamic array manipulation, the concept of heapification, and the practical application of these concepts in data structure design and management. The problem also evaluates the candidate’s problem-solving skills and attention to detail, particularly in maintaining the heap’s properties.

7. Data Structure Question: Reverse a Linked List

Task Write a function to reverse a singly linked list. The function should be able to take the head of the list as input and return the new head of the reversed list.
Input Format
  • The head of a singly linked list.
  • The number of nodes in the list is in the range [1,5000][1,5000].
  • Node values are within the range [−5000,5000][−5000,5000].
Output Format The new head of the reversed linked list.
Sample Input Consider a linked list: 1 -> 2 -> 3 -> 4 -> 5
Sample Output Reversed linked list: 5 -> 4 -> 3 -> 2 -> 1

Suggested Answer

# Definition for singly-linked list.

class ListNode:

    def __init__(self, val=0, next=None):

        self.val = val = next

def reverseLinkedList(head):

    prev, curr = None, head

    while curr:

        nextTemp = = prev

        prev = curr

        curr = nextTemp

    return prev

# Example usage

# Assuming the linked list is already created and head points to the first node

# newHead = reverseLinkedList(head)

Code Explanation

This code snippet define a function reverseLinkedList to reverse a singly linked list. It uses a simple iterative approach, maintaining three-pointers (prev, curr, and nextTemp) to reverse the direction of the links between nodes. 

The prev pointer tracks the new reversed list, curr traverses the original list, and nextTemp temporarily stores the next node to visit. This process continues until curr becomes None, indicating the end of the list, at which point prev points to the new head of the reversed list.

Common Mistakes to Watch Out For
  • Losing the reference to the rest of the list while reversing the links, which can result in a memory leak in languages with manual memory management.
  • Incorrectly handling the head and tail of the list, especially updating the new head’s next to None.
  • Forgetting to return the new head of the list after reversal.
  • How would your approach change if this were a doubly linked list?
  • Discuss how to reverse the list recursively and compare the space complexity of both approaches.
What the question tests This question assesses the candidate’s understanding of linked lists, a fundamental data structure in computer science. It tests the ability to manipulate pointers (or references) to modify the list structure without additional memory for data storage, showcasing in-place reversal. The task evaluates problem-solving skills, understanding of data structure manipulation, and the ability to think through the implications of pointer operations.

8. Arrays Question: Find All Numbers Disappeared in an Array

Task Given an array nums of n integers where nums[i] is in the range [1, n], some elements appear twice and others appear once. Find all the elements of [1, n] inclusive that do not appear in this array. Could you do it without extra space and in O(n) runtime? You may assume the returned list does not count as extra space.
Input Format
  • An array nums with n integers.
Output Format
  • An array of integers that are missing from the array.
Sample Input nums = [4,3,2,7,8,2,3,1]
Sample Output [5, 6]

Suggested Answer

def findDisappearedNumbers(nums):

    for i in range(len(nums)):

        new_index = abs(nums[i]) – 1

        if nums[new_index] > 0:

            nums[new_index] *= -1

    result = []

    for i in range(1, len(nums) + 1):

        if nums[i – 1] > 0:


    return result

Code Explanation

This solution iterates over the numbers in the array, using the value of each element (after making it positive to handle previously altered values) as an index to flag the presence of numbers by negating the value at that index in the array. 

After this process, any value in the array that remains positive indicates that its index + 1 (to account for the 0-based indexing) is missing from the original array. This way, we use the original array itself to track which numbers have been seen without requiring additional space for another data structure.

Common Mistakes to Watch Out For
  • Forgetting to account for the array’s 0-based indexing when marking numbers as seen.
  • Missing to convert the numbers back to positive when checking which ones did not appear.
  • Attempting to use extra space, which violates the problem’s constraints for an in-place solution.
  • Can you think of a way to solve this problem if the numbers in nums were not guaranteed to be in the range [1, n]?
What the question tests This question is an excellent test of a candidate’s ability to manipulate arrays in place to track information efficiently. It also highlights the importance of understanding and working within constraint boundaries to devise creative solutions.


In this article, we’ve explored a comprehensive range of coding interview questions essential for hiring managers looking to assess software engineering candidates effectively, from language-specific problems that test candidates’ proficiency in Java, Python, and JavaScript to algorithm and data structure questions that delve into their problem-solving skills and understanding of core computer science concepts. 

These coding interview questions are designed to test technical skills and gauge candidates’ analytical thinking, approach to solving complex problems, and ability to work under pressure. 

Using Interview Zen as a platform for crafting and managing these coding interview questions offers numerous advantages. Interview Zen simplifies the interview process, making it more efficient and effective by providing tools to create comprehensive, tailored coding challenges. This approach ensures that hiring managers can accurately gauge a candidate’s abilities in a manner that’s both fair and consistent.

We strongly encourage hiring managers and recruiters to use Interview Zen for their coding interview needs. The platform’s intuitive design and comprehensive suite of features make it an invaluable tool for anyone looking to streamline their technical hiring process. 

By leveraging Interview Zen, you can create detailed, customized coding interviews that accurately assess the competencies and potential of your software engineering candidates.

Start using Interview Zen today to enhance your hiring process, reduce the time spent on candidate assessments, and ensure you attract and identify the best talent for your software development team. 


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