Top 8 Coding Interview Questions Every Hiring Manager Should Ask

Posted on February 23 2024 by Interview Zen Team

Introduction

Coding interviews have become an important part of the hiring process in the competitive software development landscape. With the increasing demand for skilled developers and the need to assess technical competency beyond resumes and portfolios, coding interviews provide a standardized way to evaluate problem-solving abilities, algorithmic thinking, and code quality.

According to a 2024 survey by CoderPad, 93% of companies use coding interviews as part of their technical hiring process. However, designing effective coding interview questions that accurately assess candidate capabilities while being fair and relevant to actual job requirements remains a challenge for many hiring managers.

This comprehensive guide provides hiring managers with essential coding interview questions that test fundamental programming concepts, problem-solving skills, and practical application across various difficulty levels and domains.

The Purpose of Coding Interviews

Coding interviews serve multiple purposes in the hiring process:

  • Technical competency: Verify candidates can write working code
  • Problem-solving approach: Assess how candidates break down complex problems
  • Communication skills: Evaluate ability to explain technical concepts
  • Code quality: Review coding style, readability, and best practices
  • Adaptability: Test how candidates handle feedback and iterate on solutions

Top 8 Coding Interview Questions

1. Two Sum Problem (Easy)

Problem: Given an array of integers and a target sum, return indices of two numbers that add up to the target.

Why This Question: Tests basic array manipulation, hash map usage, and optimization thinking.

Example Solution:

def two_sum(nums, target):
    """
    Time Complexity: O(n)
    Space Complexity: O(n)
    """
    num_map = {}
    
    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_map:
            return [num_map[complement], i]
        num_map[num] = i
    
    return []

# Test case
nums = [2, 7, 11, 15]
target = 9
print(two_sum(nums, target))  # Output: [0, 1]

What to Look For:

  • Initial brute force approach (O(n²)) vs optimized hash map solution (O(n))
  • Proper handling of edge cases
  • Clear variable naming and code structure

2. Valid Palindrome (Easy-Medium)

Problem: Determine if a string is a palindrome, considering only alphanumeric characters and ignoring case.

Why This Question: Tests string manipulation, two-pointer technique, and attention to requirements.

Example Solution:

def is_palindrome(s):
    """
    Time Complexity: O(n)
    Space Complexity: O(1)
    """
    left, right = 0, len(s) - 1
    
    while left < right:
        # Skip non-alphanumeric characters
        while left < right and not s[left].isalnum():
            left += 1
        while left < right and not s[right].isalnum():
            right -= 1
        
        # Compare characters (case-insensitive)
        if s[left].lower() != s[right].lower():
            return False
        
        left += 1
        right -= 1
    
    return True

# Test cases
print(is_palindrome("A man, a plan, a canal: Panama"))  # True
print(is_palindrome("race a car"))  # False

What to Look For:

  • Proper handling of special characters and case sensitivity
  • Efficient two-pointer approach vs string preprocessing
  • Edge case consideration (empty strings, single characters)

3. Binary Tree Maximum Depth (Medium)

Problem: Find the maximum depth of a binary tree.

Why This Question: Tests tree traversal understanding, recursion, and algorithmic thinking.

Example Solution:

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def max_depth(root):
    """
    Recursive solution
    Time Complexity: O(n)
    Space Complexity: O(h) where h is height
    """
    if not root:
        return 0
    
    left_depth = max_depth(root.left)
    right_depth = max_depth(root.right)
    
    return 1 + max(left_depth, right_depth)

# Iterative solution using BFS
from collections import deque

def max_depth_iterative(root):
    if not root:
        return 0
    
    queue = deque([(root, 1)])
    max_depth = 0
    
    while queue:
        node, depth = queue.popleft()
        max_depth = max(max_depth, depth)
        
        if node.left:
            queue.append((node.left, depth + 1))
        if node.right:
            queue.append((node.right, depth + 1))
    
    return max_depth

What to Look For:

  • Understanding of tree structure and traversal
  • Both recursive and iterative approaches
  • Proper handling of null/empty trees

4. Valid Parentheses (Medium)

Problem: Given a string containing parentheses, brackets, and braces, determine if they are valid (properly opened and closed in correct order).

Why This Question: Tests stack data structure understanding and pattern recognition.

Example Solution:

def is_valid(s):
    """
    Time Complexity: O(n)
    Space Complexity: O(n)
    """
    stack = []
    mapping = {')': '(', '}': '{', ']': '['}
    
    for char in s:
        if char in mapping:  # Closing bracket
            if not stack or stack.pop() != mapping[char]:
                return False
        else:  # Opening bracket
            stack.append(char)
    
    return len(stack) == 0

# Test cases
print(is_valid("()"))        # True
print(is_valid("()[]{}"))    # True
print(is_valid("(]"))        # False
print(is_valid("([)]"))      # False
print(is_valid("{[]}"))      # True

What to Look For:

  • Correct use of stack data structure
  • Proper mapping of opening/closing brackets
  • Edge case handling (empty string, unmatched brackets)

5. Merge Two Sorted Arrays (Medium)

Problem: Merge two sorted arrays into one sorted array in-place.

Why This Question: Tests array manipulation, two-pointer technique, and in-place algorithms.

Example Solution:

def merge(nums1, m, nums2, n):
    """
    Merge nums2 into nums1 in-place
    Time Complexity: O(m + n)
    Space Complexity: O(1)
    """
    # Start from the end to avoid overwriting
    i, j, k = m - 1, n - 1, m + n - 1
    
    while i >= 0 and j >= 0:
        if nums1[i] > nums2[j]:
            nums1[k] = nums1[i]
            i -= 1
        else:
            nums1[k] = nums2[j]
            j -= 1
        k -= 1
    
    # Copy remaining elements from nums2
    while j >= 0:
        nums1[k] = nums2[j]
        j -= 1
        k -= 1

# Test case
nums1 = [1, 2, 3, 0, 0, 0]
nums2 = [2, 5, 6]
merge(nums1, 3, nums2, 3)
print(nums1)  # [1, 2, 2, 3, 5, 6]

What to Look For:

  • Understanding of in-place algorithm constraints
  • Backwards iteration to avoid overwriting
  • Handling of remaining elements

6. Longest Substring Without Repeating Characters (Medium-Hard)

Problem: Find the length of the longest substring without repeating characters.

Why This Question: Tests sliding window technique, hash map usage, and optimization skills.

Example Solution:

def length_of_longest_substring(s):
    """
    Time Complexity: O(n)
    Space Complexity: O(min(m, n)) where m is charset size
    """
    char_map = {}
    left = 0
    max_length = 0
    
    for right, char in enumerate(s):
        if char in char_map and char_map[char] >= left:
            # Move left pointer past the duplicate
            left = char_map[char] + 1
        
        char_map[char] = right
        max_length = max(max_length, right - left + 1)
    
    return max_length

# Test cases
print(length_of_longest_substring("abcabcbb"))  # 3 ("abc")
print(length_of_longest_substring("bbbbb"))     # 1 ("b")
print(length_of_longest_substring("pwwkew"))    # 3 ("wke")

What to Look For:

  • Sliding window technique understanding
  • Efficient tracking of character positions
  • Proper window resizing logic

7. Reverse Linked List (Medium)

Problem: Reverse a singly linked list iteratively and recursively.

Why This Question: Tests linked list manipulation and pointer management.

Example Solution:

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

def reverse_list_iterative(head):
    """
    Iterative solution
    Time Complexity: O(n)
    Space Complexity: O(1)
    """
    prev = None
    current = head
    
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    
    return prev

def reverse_list_recursive(head):
    """
    Recursive solution
    Time Complexity: O(n)
    Space Complexity: O(n)
    """
    if not head or not head.next:
        return head
    
    reversed_head = reverse_list_recursive(head.next)
    head.next.next = head
    head.next = None
    
    return reversed_head

What to Look For:

  • Understanding of pointer manipulation
  • Both iterative and recursive approaches
  • Proper handling of edge cases (empty list, single node)

8. Binary Search (Easy-Medium)

Problem: Implement binary search to find target value in sorted array.

Why This Question: Tests fundamental algorithm understanding and attention to boundary conditions.

Example Solution:

def binary_search(nums, target):
    """
    Time Complexity: O(log n)
    Space Complexity: O(1)
    """
    left, right = 0, len(nums) - 1
    
    while left <= right:
        mid = left + (right - left) // 2  # Avoid overflow
        
        if nums[mid] == target:
            return mid
        elif nums[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1

def binary_search_recursive(nums, target, left=0, right=None):
    """
    Recursive implementation
    """
    if right is None:
        right = len(nums) - 1
    
    if left > right:
        return -1
    
    mid = left + (right - left) // 2
    
    if nums[mid] == target:
        return mid
    elif nums[mid] < target:
        return binary_search_recursive(nums, target, mid + 1, right)
    else:
        return binary_search_recursive(nums, target, left, mid - 1)

# Test case
nums = [1, 3, 5, 7, 9, 11]
print(binary_search(nums, 7))  # Output: 3

What to Look For:

  • Correct boundary handling (left <= right)
  • Proper midpoint calculation to avoid integer overflow
  • Understanding of logarithmic time complexity

Interview Best Practices

For Hiring Managers:

  1. Start with easier questions to build candidate confidence
  2. Allow time for questions - let candidates clarify requirements
  3. Focus on problem-solving process over perfect solutions
  4. Encourage communication - ask candidates to think aloud
  5. Provide hints when stuck to see how candidates respond to guidance

What to Evaluate:

  • Problem-solving approach: How they break down problems
  • Code quality: Readability, structure, and naming conventions
  • Testing mindset: Do they consider edge cases and test inputs?
  • Optimization awareness: Can they improve time/space complexity?
  • Communication skills: Can they explain their reasoning clearly?

Red Flags:

  • Inability to write basic syntax in their claimed language
  • Not asking clarifying questions about requirements
  • Poor variable naming or code organization
  • Ignoring edge cases entirely
  • Cannot explain their solution or reasoning

Conclusion

Effective coding interviews require careful question selection that balances difficulty with practical relevance. These 8 questions cover fundamental concepts while allowing candidates to demonstrate problem-solving skills and technical competency.

Remember that coding interviews are just one part of the evaluation process. The best candidates combine technical skills with communication abilities, learning mindset, and practical experience relevant to your specific needs.

Consider using Interview Zen’s technical interview platform to create structured coding assessments and observe candidates’ real-time problem-solving approaches during technical interviews.