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🚀 Coding Interview Questions with Answers (Part 19)
1️⃣8️⃣1️⃣ How Do You Reverse a String?
Answer:
Reversing a string means arranging its characters in the opposite order.
Example:
Input: "hello"
Output: "olleh"
Python:
Time Complexity: O(n)
Space Complexity: O(n)
1️⃣8️⃣2️⃣ How Do You Find the Largest Element in an Array?
Answer:
Traverse the array while keeping track of the largest value found so far.
Example:
Input: [10, 25, 7, 42, 18]
Output: 42
Python:
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣3️⃣ How Do You Find the Second Largest Element in an Array?
Answer:
Maintain two variables: one for the largest element and another for the second largest. Update them while traversing the array.
Example:
Input: [10, 25, 7, 42, 18]
Output: 25
Python:
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣4️⃣ How Do You Check Whether a String is a Palindrome?
Answer:
A palindrome is a string that reads the same forward and backward.
Examples:
"madam" → Palindrome
"level" → Palindrome
"hello" → Not a palindrome
Python:
Time Complexity: O(n)
1️⃣8️⃣5️⃣ How Do You Find Duplicate Elements in an Array?
Answer:
Use a set to keep track of elements that have already appeared. If an element is already present in the set, it is a duplicate.
Example:
Input: [1, 2, 3, 2, 4, 1]
Output: [1, 2]
Python:
Average Time Complexity: O(n)
Space Complexity: O(n)
1️⃣8️⃣6️⃣ How Do You Remove Duplicates from an Array?
Answer:
A common approach is to use a set, which stores only unique values.
Example:
Input: [1, 2, 2, 3, 3, 4]
Output: [1, 2, 3, 4]
Python:
If the original order must be preserved:
Average Time Complexity: O(n)
1️⃣8️⃣7️⃣ How Do You Find the Missing Number in an Array?
Answer:
If an array contains numbers from "1" to "n" with one number missing, calculate the expected sum and subtract the actual sum.
Example:
Input: [1, 2, 4, 5]
Output: 3
Python:
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣8️⃣ How Do You Merge Two Sorted Arrays?
Answer:
Use two pointers to compare elements from both arrays and add the smaller element to the result.
Example:
Input:
[1, 3, 5]
[2, 4, 6]
Output:
[1, 2, 3, 4, 5, 6]
Python:
1️⃣8️⃣1️⃣ How Do You Reverse a String?
Answer:
Reversing a string means arranging its characters in the opposite order.
Example:
Input: "hello"
Output: "olleh"
Python:
text = "hello"
reversed_text = text[::-1]
print(reversed_text)
Time Complexity: O(n)
Space Complexity: O(n)
1️⃣8️⃣2️⃣ How Do You Find the Largest Element in an Array?
Answer:
Traverse the array while keeping track of the largest value found so far.
Example:
Input: [10, 25, 7, 42, 18]
Output: 42
Python:
numbers = [10, 25, 7, 42, 18]
largest = numbers[0]
for num in numbers:
if num > largest:
largest = num
print(largest)
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣3️⃣ How Do You Find the Second Largest Element in an Array?
Answer:
Maintain two variables: one for the largest element and another for the second largest. Update them while traversing the array.
Example:
Input: [10, 25, 7, 42, 18]
Output: 25
Python:
numbers = [10, 25, 7, 42, 18]
largest = second = float('-inf')
for num in numbers:
if num > largest:
second = largest
largest = num
elif largest > num > second:
second = num
print(second)
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣4️⃣ How Do You Check Whether a String is a Palindrome?
Answer:
A palindrome is a string that reads the same forward and backward.
Examples:
"madam" → Palindrome
"level" → Palindrome
"hello" → Not a palindrome
Python:
text = "madam"
if text == text[::-1]:
print("Palindrome")
else:
print("Not a palindrome")
Time Complexity: O(n)
1️⃣8️⃣5️⃣ How Do You Find Duplicate Elements in an Array?
Answer:
Use a set to keep track of elements that have already appeared. If an element is already present in the set, it is a duplicate.
Example:
Input: [1, 2, 3, 2, 4, 1]
Output: [1, 2]
Python:
numbers = [1, 2, 3, 2, 4, 1]
seen = set()
duplicates = set()
for num in numbers:
if num in seen:
duplicates.add(num)
else:
seen.add(num)
print(duplicates)
Average Time Complexity: O(n)
Space Complexity: O(n)
1️⃣8️⃣6️⃣ How Do You Remove Duplicates from an Array?
Answer:
A common approach is to use a set, which stores only unique values.
Example:
Input: [1, 2, 2, 3, 3, 4]
Output: [1, 2, 3, 4]
Python:
numbers = [1, 2, 2, 3, 3, 4]
unique_numbers = list(set(numbers))
print(unique_numbers)
If the original order must be preserved:
unique_numbers = list(dict.fromkeys(numbers))
Average Time Complexity: O(n)
1️⃣8️⃣7️⃣ How Do You Find the Missing Number in an Array?
Answer:
If an array contains numbers from "1" to "n" with one number missing, calculate the expected sum and subtract the actual sum.
Example:
Input: [1, 2, 4, 5]
Output: 3
Python:
numbers = [1, 2, 4, 5]
n = 5
expected = n * (n + 1) // 2
missing = expected - sum(numbers)
print(missing)
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣8️⃣8️⃣ How Do You Merge Two Sorted Arrays?
Answer:
Use two pointers to compare elements from both arrays and add the smaller element to the result.
Example:
Input:
[1, 3, 5]
[2, 4, 6]
Output:
[1, 2, 3, 4, 5, 6]
Python:
a = [1, 3, 5]
b = [2, 4, 6]
i = j = 0
result = []
while i < len(a) and j < len(b):
if a[i] < b[j]:
result.append(a[i])
i += 1
else:
result.append(b[j])
j += 1
while i < len(a):
result.append(a[i])
i += 1
while j < len(b):
result.append(b[j])
j += 1
print(result)
❤3
Time Complexity: O(n + m)
Space Complexity: O(n + m)
1️⃣8️⃣9️⃣ How Do You Check if Two Strings are Anagrams?
Answer:
Two strings are anagrams if they contain the same characters with the same frequencies, but possibly in a different order.
Example:
"listen" → "silent"
Both contain the same characters, so they are anagrams.
Python:
Time Complexity: O(n log n)
A frequency-count approach can achieve O(n) average time.
1️⃣9️⃣0️⃣ How Do You Find the First Non-Repeating Character?
Answer:
Count the frequency of every character, then scan the string again and return the first character whose frequency is "1".
Example:
Input: "swiss"
Output: "w"
Python:
Time Complexity: O(n)
Space Complexity: O(k), where "k" is the number of distinct characters.
🔥 Double Tap ❤️ For Part-20
Space Complexity: O(n + m)
1️⃣8️⃣9️⃣ How Do You Check if Two Strings are Anagrams?
Answer:
Two strings are anagrams if they contain the same characters with the same frequencies, but possibly in a different order.
Example:
"listen" → "silent"
Both contain the same characters, so they are anagrams.
Python:
str1 = "listen"
str2 = "silent"
if sorted(str1) == sorted(str2):
print("Anagrams")
else:
print("Not Anagrams")
Time Complexity: O(n log n)
A frequency-count approach can achieve O(n) average time.
1️⃣9️⃣0️⃣ How Do You Find the First Non-Repeating Character?
Answer:
Count the frequency of every character, then scan the string again and return the first character whose frequency is "1".
Example:
Input: "swiss"
Output: "w"
Python:
from collections import Counter
text = "swiss"
count = Counter(text)
for char in text:
if count[char] == 1:
print(char)
break
Time Complexity: O(n)
Space Complexity: O(k), where "k" is the number of distinct characters.
🔥 Double Tap ❤️ For Part-20
❤8
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🚀 Coding Interview Questions with Answers (Part 20)
1️⃣9️⃣1️⃣ What is the Two Sum Problem?
Answer:
The Two Sum problem asks you to find two elements in an array whose sum equals a given target.
Example:
Input: [2][7][11][15]
Target: 9
Output: [2][7]
A Hash Map can be used to store previously seen values and find the required complement efficiently.
Time Complexity: O(n)
Space Complexity: O(n)
1️⃣9️⃣2️⃣ What is the Longest Substring Without Repeating Characters Problem?
Answer:
The goal is to find the longest substring that contains no repeated characters.
Example:
Input: "abcbb"
Output: 3
The longest substring is "abc".
A Sliding Window with a Hash Set or Hash Map can solve this efficiently.
Time Complexity: O(n)
Space Complexity: O(k)
1️⃣9️⃣3️⃣ What is the Longest Common Subsequence (LCS) Problem?
Answer:
LCS finds the longest sequence that appears in the same order in two strings, but the characters do not need to be adjacent.
Example:
Input: "abcde" and "ace"
Output: "ace"
Dynamic Programming is commonly used to solve this problem.
Time Complexity: O(m × n)
Space Complexity: O(m × n)
1️⃣9️⃣4️⃣ What is the Longest Increasing Subsequence (LIS) Problem?
Answer:
LIS finds the longest subsequence of an array where the elements are in strictly increasing order.
Example:
Input: [10][9][2][5][3][7][101][18]
Output: 4
One possible LIS is: [2][3][7][101]
It can be solved using Dynamic Programming or an optimized Binary Search approach.
Time Complexity: O(n log n) using the optimized approach.
1️⃣9️⃣5️⃣ What is the Maximum Subarray Sum Problem?
Answer:
The goal is to find the contiguous subarray with the largest possible sum.
Example:
Input: [-2][1][-3][4][-1][2][1][-5][4]
Output: 6
The maximum-sum subarray is: [4][-1][2][1]
Kadane's Algorithm can solve this efficiently.
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣9️⃣6️⃣ What is the Merge Intervals Problem?
Answer:
The Merge Intervals problem requires combining overlapping intervals into a single interval.
Example:
Input: [[1,3][2,6][8,10][9,12]]
Output: [[1,6][8,12]]
The typical approach is to sort the intervals by their starting value and then merge overlapping intervals.
Time Complexity: O(n log n)
Space Complexity: O(n)
1️⃣9️⃣7️⃣ What is the Trapping Rain Water Problem?
Answer:
The problem asks you to calculate how much rainwater can be trapped between bars of different heights.
Example:
Input: [0][1][0][2][1][0][1][3][2][1][2][1]
Output: 6
A Two Pointers approach can solve this problem efficiently by tracking the maximum height from both sides.
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣9️⃣8️⃣ What is the Median of Two Sorted Arrays Problem?
Answer:
The goal is to find the median of two sorted arrays without necessarily merging them completely.
Example:
Input: [1][3] and [2]
Output: 2
An optimized solution uses Binary Search to partition the two arrays correctly.
Time Complexity: O(log(min(m,n)))
Space Complexity: O(1)
1️⃣9️⃣9️⃣ What is the LRU Cache Problem?
Answer:
LRU stands for Least Recently Used.
1️⃣9️⃣1️⃣ What is the Two Sum Problem?
Answer:
The Two Sum problem asks you to find two elements in an array whose sum equals a given target.
Example:
Input: [2][7][11][15]
Target: 9
Output: [2][7]
A Hash Map can be used to store previously seen values and find the required complement efficiently.
Time Complexity: O(n)
Space Complexity: O(n)
1️⃣9️⃣2️⃣ What is the Longest Substring Without Repeating Characters Problem?
Answer:
The goal is to find the longest substring that contains no repeated characters.
Example:
Input: "abcbb"
Output: 3
The longest substring is "abc".
A Sliding Window with a Hash Set or Hash Map can solve this efficiently.
Time Complexity: O(n)
Space Complexity: O(k)
1️⃣9️⃣3️⃣ What is the Longest Common Subsequence (LCS) Problem?
Answer:
LCS finds the longest sequence that appears in the same order in two strings, but the characters do not need to be adjacent.
Example:
Input: "abcde" and "ace"
Output: "ace"
Dynamic Programming is commonly used to solve this problem.
Time Complexity: O(m × n)
Space Complexity: O(m × n)
1️⃣9️⃣4️⃣ What is the Longest Increasing Subsequence (LIS) Problem?
Answer:
LIS finds the longest subsequence of an array where the elements are in strictly increasing order.
Example:
Input: [10][9][2][5][3][7][101][18]
Output: 4
One possible LIS is: [2][3][7][101]
It can be solved using Dynamic Programming or an optimized Binary Search approach.
Time Complexity: O(n log n) using the optimized approach.
1️⃣9️⃣5️⃣ What is the Maximum Subarray Sum Problem?
Answer:
The goal is to find the contiguous subarray with the largest possible sum.
Example:
Input: [-2][1][-3][4][-1][2][1][-5][4]
Output: 6
The maximum-sum subarray is: [4][-1][2][1]
Kadane's Algorithm can solve this efficiently.
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣9️⃣6️⃣ What is the Merge Intervals Problem?
Answer:
The Merge Intervals problem requires combining overlapping intervals into a single interval.
Example:
Input: [[1,3][2,6][8,10][9,12]]
Output: [[1,6][8,12]]
The typical approach is to sort the intervals by their starting value and then merge overlapping intervals.
Time Complexity: O(n log n)
Space Complexity: O(n)
1️⃣9️⃣7️⃣ What is the Trapping Rain Water Problem?
Answer:
The problem asks you to calculate how much rainwater can be trapped between bars of different heights.
Example:
Input: [0][1][0][2][1][0][1][3][2][1][2][1]
Output: 6
A Two Pointers approach can solve this problem efficiently by tracking the maximum height from both sides.
Time Complexity: O(n)
Space Complexity: O(1)
1️⃣9️⃣8️⃣ What is the Median of Two Sorted Arrays Problem?
Answer:
The goal is to find the median of two sorted arrays without necessarily merging them completely.
Example:
Input: [1][3] and [2]
Output: 2
An optimized solution uses Binary Search to partition the two arrays correctly.
Time Complexity: O(log(min(m,n)))
Space Complexity: O(1)
1️⃣9️⃣9️⃣ What is the LRU Cache Problem?
Answer:
LRU stands for Least Recently Used.
❤1
An LRU Cache removes the item that has not been used for the longest time when the cache reaches its capacity.
A common implementation uses:
• Hash Map for O(1) lookup.
• Doubly Linked List for O(1) insertion and removal.
Time Complexity:
• Get: O(1)
• Put: O(1)
2️⃣0️⃣0️⃣ How Would You Design a URL Shortener?
Answer:
A URL shortener converts a long URL into a short, unique URL.
Example:
Long URL: https://example.com/products/category/item/12345
Short URL: https://short.ly/aB92x
A basic system can use:
1. Generate a unique ID for each URL.
2. Convert the ID into a short Base62 string.
3. Store the mapping between the short code and original URL.
4. When the short URL is requested, look up the original URL.
5. Redirect the user to the original URL.
Important Design Considerations:
• Unique short IDs
• Database design
• Caching
• Scalability
• High availability
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A common implementation uses:
• Hash Map for O(1) lookup.
• Doubly Linked List for O(1) insertion and removal.
Time Complexity:
• Get: O(1)
• Put: O(1)
2️⃣0️⃣0️⃣ How Would You Design a URL Shortener?
Answer:
A URL shortener converts a long URL into a short, unique URL.
Example:
Long URL: https://example.com/products/category/item/12345
Short URL: https://short.ly/aB92x
A basic system can use:
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