Introduction
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’ problemsolving 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 interviewing.io, 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.
In this article, we will explore the top 8 coding interview questions every hiring manager should ask. These questions cover various topics, including languagespecific problems, algorithms, data structures, and array manipulations.
Top 8 Coding Interview Questions
Let’s explore the top 8 coding interview questions:
1. Java LanguageSpecific 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 threadsafe without using the synchronized keyword on the getInstance() method. 
Constraints 

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 “DoubleChecked 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.
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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 threadsafe 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 LanguageSpecific 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. 
Constraints 

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): @wraps(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 @execution_time_logger def sample_function(): time.sleep(2) 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.
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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 nonintrusively using decorators. 
3. JavaScript LanguageSpecific 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. 
Constraints 

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 = Date.now(); } else { clearTimeout(lastFunc); lastFunc = setTimeout(function() { if ((Date.now() – lastRan) >= limit) { func.apply(context, args); lastRan = Date.now(); } }, limit – (Date.now() – 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.
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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 

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 minheap to keep track of the top k largest elements in the array. The kth largest element will be at the root of the minheap after processing all elements.
import heapq
def findKthLargest(nums, k): minHeap = [] for num in nums: heapq.heappush(minHeap, num) if len(minHeap) > k: heapq.heappop(minHeap) return minHeap[0] 
Code Explanation
This code uses a minheap to efficiently find the kth largest element in an unsorted array. By maintaining a minheap 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.
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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 

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 else: left = mid + 1 # The right half is sorted else: if nums[mid] < target <= nums[right]: left = mid + 1 else: 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.
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What the question tests  This question evaluates the candidate’s ability to adapt and apply binary search to a nonstandard problem setting, specifically searching in a rotated sorted array. It tests understanding of binary search principles, decisionmaking 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 builtin library or API for heap operations. 
Input Format 

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.heap.append(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() self._bubble_down(0) elif self.heap: maxVal = self.heap.pop() else: 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) self._bubble_down(largest) def _swap(self, i, j): self.heap[i], self.heap[j] = self.heap[j], self.heap[i] # Example usage heap = MaxHeap() heap.insert(3) heap.insert(10) heap.insert(5) heap.insert(1) heap.insert(2) print(heap.getMax()) # 10 heap.deleteMax() 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.
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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 problemsolving 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 

Constraints 

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 singlylinked list.
class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next def reverseLinkedList(head): prev, curr = None, head while curr: nextTemp = curr.next curr.next = 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 threepointers (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.
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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 inplace reversal. The task evaluates problemsolving 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 

Output Format 

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: result.append(i) 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 0based 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.
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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. 
Conclusion
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 languagespecific problems that test candidates’ proficiency in Java, Python, and JavaScript to algorithm and data structure questions that delve into their problemsolving 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.