Grokking the Coding Interview

Want to save your time with a complete coding interview preparation guide for FAANG interviews?

Grokking the Top Coding Interview Questions

Are you getting ready for those tough coding interviews at top tech companies like FAANG (Facebook, Amazon, Apple, Netflix, Google)?

I get it, preparing for these interviews can be a real pain.

You spend hours, sometimes even days, just trying to find useful resources. And even when you do, everything is scattered all over the place.

Preparing for coding interviews, especially for top companies like FAANG, can feel like trying to find a needle in a haystack.

For beginners, it’s even worse. What should you prepare? How should you prepare? Where do you even start? It feels like a never-ending maze with a whole lot of trial and error and still no guarantee of success.

Well, that’s where Grokking 75- Top Coding Interview Questions by DesignGurus.io comes in. This course is a lifesaver. It brings everything you need into one place.

You don’t need to get into the endless scrolling through random blog posts and forums, trying to piece together a coherent study plan.

With this course, everything you need is neatly organized and readily available at your fingertips. No more wasting time hunting for resources.

This course covers the 75 most common and important coding problems that you may have to face in interviews, making your preparation process smooth and efficient.

It not only streamlines the preparation process but also provides clear guidance on what to focus on and how to tackle those intimidating coding questions. It’s like having a personal mentor guiding you every step of the way

As an ex-FAANG engineer, I’ve been through the rigorous interview process and understand how challenging it can be. I have been a part of several interview panels to date.

Trust me, preparation is key, but so is a smart approach.

One thing I learned is that quality trumps quantity. Instead of trying to solve every problem out there, focus on understanding the concepts deeply.

The “Grokking 75- Top Coding Interview Questions” course by DesignGurus.io is perfect for this. When I prepared, I wished I had a resource like Grokking 75 — a complete guide with all essential details.

It compiles the most common and challenging questions asked in FAANG interviews over the past year, saving you from the hassle of hunting down resources yourself.

Here are the top five coding concepts you need to master: Arrays, Hashmaps, Sliding Window, Two Pointers, and Matrix.

Don’t worry if these sound a bit intimidating; I’m here to break them down for you.

1. Arrays

Arrays are like a collection of boxes, where each box holds a value. You can think of them as a simple list of items.

For example, an array of numbers might look like this: [1, 2, 3, 4, 5].

Arrays are super important because they let you store and access data quickly. Interviewers love asking about arrays because they’re fundamental and can be used in a ton of different problems.

Coding Problem: Roman to Integer Conversion

Convert the Roman numeral string ‘s’ into its equivalent integer.

Roman numerals use combinations of seven symbols: I, V, X, L, C, D, and M, representing values 1, 5, 10, 50, 100, 500, and 1000 respectively.

For example, II is equivalent to 1, and XI is equivalent to 11 (X + I). In some cases, a smaller numeral before a larger numeral indicates subtraction (e.g., IV = 4).

class Solution:
def romanToInt(self, s: str) -> int:
# Map of Roman numerals to integers
roman_map = {‘I’: 1, ‘V’: 5, ‘X’: 10, ‘L’: 50, ‘C’: 100, ‘D’: 500, ‘M’: 1000}

result = 0 # To store the final result
n = len(s)

# Iterate through the string
for i in range(n):
# Get the value of the current Roman numeral
value = roman_map[s[i]]

# Check if the current numeral is smaller than the next one
if i < n – 1 and value < roman_map[s[i + 1]]:
result -= value # Subtract the value
else:
result += value # Add the value

return result # Return the final result

# Testing the solution
solution = Solution()
print(solution.romanToInt(“XLII”)) # 42
print(solution.romanToInt(“CXCIV”)) # 194
print(solution.romanToInt(“MMMCDXLIV”)) # 3444

Tips:

Practice looping through arrays to get comfortable.Learn about common operations like sorting and searching.

2. Hashmaps

A hashmap (or dictionary) is like a real-life dictionary that lets you store data in pairs — a key and its value.

You look up a word (key) and get its definition (value). They are great for problems where you need to quickly find, add, or remove items based on a key.

Think of it like a phone book where you look up a name (key) to find a phone number (value).

Coding Problem: Word Pattern

Given a pattern and a string s, return true if the string s follows the same pattern.

Here, the following the pattern means each character in the pattern should map to a single word in s, and each word in s should map to a single character in the pattern.

class Solution:
def wordPattern(self, pattern: str, s: str) -> bool:
# Split the string s into words
words = s.split()
# If lengths of pattern and words do not match, return false
if len(pattern) != len(words):
return False

# Initialize dictionaries to keep track of mappings
char_to_word = {}
word_to_char = {}

# Iterate over each character and word
for char, word in zip(pattern, words):
# Check if the character is already mapped
if char in char_to_word:
# If mapped word doesn’t match the current word, return false
if char_to_word[char] != word:
return False
else:
# Map the character to the word
char_to_word[char] = word

# Check if the word is already mapped
if word in word_to_char:
# If mapped character doesn’t match the current character, return false
if word_to_char[word] != char:
return False
else:
# Map the word to the character
word_to_char[word] = char

# If all checks pass, return true
return True

if __name__ == “__main__”:
solution = Solution()
# Example 1
print(solution.wordPattern(“eegg”, “dog dog cat cat”)) # true
# Example 2
print(solution.wordPattern(“abca”, “one two three four”)) # false
# Example 3
print(solution.wordPattern(“abacac”, “dog cat dog mouse dog mouse”)) # true

Tips:

Get used to inserting, deleting, and finding items quickly.Understand how hashmaps help with fast lookups compared to lists.

3. Two Pointers

The two pointers technique involves using two pointers to iterate through a data structure, often to find pairs or solve problems more efficiently.

Imagine you have two markers and you place them at different positions in an array. You move these markers towards each other or in the same direction to find a solution. This technique is often used to solve problems involving pairs or subarrays.

Coding Problem: Pair with Target Sum

Given an array of numbers sorted in ascending order and a target sum, find a pair in the array whose sum is equal to the given target.

Write a function to return the indices of the two numbers (i.e. the pair) such that they add up to the given target. If no such pair exists return [-1, -1].

class Solution:
def search(self, arr, target_sum):
left, right = 0, len(arr) – 1
while(left < right):
current_sum = arr[left] + arr[right] if current_sum == target_sum:
return [left, right]

if target_sum > current_sum:
left += 1 # we need a pair with a bigger sum
else:
right -= 1 # we need a pair with a smaller sum
return [-1, -1]

def main():
sol = Solution();
print(sol.search([1, 2, 3, 4, 6], 6))
print(sol.search([2, 5, 9, 11], 11))
main()

Tips:

Start and End: Often, one pointer starts at the beginning and the other at the end.Move Towards Each Other: Move the pointers based on the conditions you set (e.g., sum too high, move right pointer left).

4. Sliding Window

The sliding window technique involves moving a window (a subset of the array) across the data structure to solve problems efficiently.

Think of a window that slides over a portion of an array. You move this window one step at a time to check different parts of the array without starting from scratch each time. This is useful for problems where you need to find a subarray that meets certain criteria.

Coding Problem: Maximum Average Subarray

Given an array of integers and an integer k, find a contiguous subarray of length k that has the highest average value, and return this maximum average value.

class Solution:
def findMaxAverage(self, nums, k):
n = len(nums)
# Compute the sum of the first ‘k’ elements
max_sum = sum(nums[:k])
current_sum = max_sum
# Slide the window across the array
for i in range(k, n):
current_sum += nums[i] – nums[i – k] max_sum = max(max_sum, current_sum)

return max_sum / k

# Test the solution with example inputs
solution = Solution()

# Example 1
nums1 = [1, 2, 3, 4, 5, 6]k1 = 2
print(“Expected: 5.5, Output:”, solution.findMaxAverage(nums1, k1))

# Example 2
nums2 = [0, 1, 1, 3, -1, 10, -2]k2 = 3
print(“Expected: 4.0, Output:”, solution.findMaxAverage(nums2, k2))

# Example 3
nums3 = [-5, -2, 0, 3, 9, -1, 2]k3 = 4
print(“Expected: 3.25, Output:”, solution.findMaxAverage(nums3, k3))

Tips:

Draw trees to understand their structure.Practice different traversal methods to visit all nodes.

5. Matrix

A matrix is a two-dimensional array where data is arranged in rows and columns.

Think of a matrix like a grid or a table. Each cell in the matrix can be accessed using two indices: one for the row and one for the column. Matrices are used to solve problems related to grids, such as pathfinding, image processing, and more.

Coding Problem: Set Matrix Zeroes

Given a 2D grid of numbers called matrix, if any number in the grid is 0, set the entire row and column containing that zero to zeros.

The grid should be modified in place without using any extra grid.

class Solution:
def setZeroes(self, matrix):
is_col = False
R = len(matrix)
C = len(matrix[0])

# First pass: mark rows and columns that need to be zeroed
for i in range(R):
if matrix[i][0] == 0:
is_col = True # Mark the first column for zeroing
for j in range(1, C):
if matrix[i][j] == 0:
matrix[0][j] = 0 # Mark the top cell of this column
matrix[i][0] = 0 # Mark the start cell of this row

# Second pass: use the marks to set elements to zero
for i in range(1, R):
for j in range(1, C):
if matrix[i][0] == 0 or matrix[0][j] == 0:
matrix[i][j] = 0 # Set cell to zero if its row or column is marked

# See if the first row needs to be set to zero as well
if matrix[0][0] == 0:
for j in range(C):
matrix[0][j] = 0

# See if the first column needs to be set to zero as well
if is_col:
for i in range(R):
matrix[i][0] = 0

return matrix # Return the modified matrix

if __name__ == “__main__”:
solution = Solution()

matrix1 = [[2, 3, 4], [5, 0, 6], [7, 8, 9]] print(solution.setZeroes(matrix1))
# Expected: [[2, 0, 4], [0, 0, 0], [7, 0, 9]]

matrix2 = [[0, 2, 3], [4, 5, 6], [7, 8, 9]] print(solution.setZeroes(matrix2))
# Expected: [[0, 0, 0], [0, 5, 6], [0, 8, 9]]

matrix3 = [[1, 2, 3, 7], [4, 0, 6, 8], [0, 8, 9, 6], [1, 4, 6, 4]] print(solution.setZeroes(matrix3))
# Expected: [[0, 0, 3, 7], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 6, 4]]

Tips:

Accessing Elements: Use matrix[row][col] to access elements.Nested Loops: Often, nested loops are used to iterate through the matrix.Common Problems: Pathfinding (like in a maze), searching for elements, or processing images.

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What’s Inside?

The Grokking 75- Top Coding Interview Questions course covers the following concepts:

Arrays: Master problems involving finding max/min values, reversing arrays, and more.Hashmaps: Learn to count items, find duplicates, and perform fast lookups.LinkedLists: Get comfortable with reversing lists, finding the middle, and other key operations.Binary Trees: Traverse trees, find their height, and understand their structure.Graphs: Discover shortest paths, detect cycles, and traverse graphs with confidence.Heap/Priority Queue: Understand how to manage dynamic sets of data and solve problems like finding the k-th largest element.Backtracking: Learn to solve complex problems by exploring all possible options, like in puzzles or game solutions.Dynamic Programming (DP): Master techniques to break down problems into simpler subproblems, such as the famous Fibonacci sequence or knapsack problem.Trie: Get comfortable with this special tree-like structure for efficient retrieval of keys in datasets, perfect for prefix matching problems.Intervals: Handle problems involving ranges, such as merging overlapping intervals or finding free time slots.Divide & Conquer: Learn to break problems into smaller, more manageable parts and solve them recursively, like in merge sort or quick sort.Stack: Understand this LIFO (Last In, First Out) data structure for problems like balancing parentheses or reversing strings.Matrix: Get a grip on 2D arrays and solve problems like rotating matrices or finding paths in a grid.Two Pointers: Master this technique for problems involving arrays or linked lists, such as finding pairs that sum up to a target.Sliding Window: Learn this efficient technique to handle problems involving subarrays or substrings, such as finding the longest substring without repeating characters.

So, how do you master these concepts? Here are a few tips:

Practice, Practice, Practice: Use platforms like LeetCode, HackerRank, or the Grokking course to solve problems regularly.Understand the Basics: Make sure you understand the fundamentals before jumping into complex problems. I also offer specialized courses on recursion, dynamic programming, data structures and algorithms, and object-oriented design.Study Efficiently: Focus on the most common interview problems. The Grokking course is great for this.Mock Interviews: Practice with friends or use mock interview platforms to get a feel for the real thing. Check out the mock interview sessions by DesignGurus.io to book your one-on-one discussions with industry experts.

Bottomline

Preparing for coding interviews at top tech companies like FAANG can be a daunting task, but it doesn’t have to be. Using a smart approach for interview preparation can save you a lot of time and effort.

By focusing on key coding patterns, you can build a solid foundation that will help you tackle a wide range of problems.

For regular posts on coding and system design interviews, check out the DesignGurus.io blog.

75 Top Coding Questions: Prepare for FAANG Interviews Like a Pro was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.

​ Level Up Coding – Medium

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