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Preparing for data structures and algorithms interviews at MAANG

10 min read

Jan 16, 2025

content

Revisiting the data structures

Diverse data structure interview questions

Choosing the right data structure

Preparing for the algorithms

Optimized algorithms

Example: The Two Sum problem

Naive solution

Improved solution

Optimized solution

Algorithmic paradigms

Practice makes perfect

Common MAANG coding interview questions

Mock interviews

Conclusion

Getting a job at a MAANG company is the dream for many software engineers, but their strict hiring criteria and rigorous coding interviews make this a challenging feat. Recruiters at these companies look for developers with a deep understanding of data structures, algorithms, and efficient coding practices. If you plan to appear in a technical interview at any of these companies, ensure you have a strong grip on selecting the right data structures and implementing optimized algorithms.

In this blog, we’ll explore various strategies to ace the coding interviews at MAANG.

We’ll discuss basic and advanced data structures commonly featured in coding interviews.
Additionally, we’ll cover optimized algorithms and algorithmic paradigms that help interviewees devise solutions for complex problems.
Finally, we’ll emphasize the importance of practicing for interviews, including attempting mock interviews.

Revisiting the data structures#

Data structures are a fundamental component in coding interviews at MAANG. Interviewers pose such problems to test a candidate’s ability to choose the most appropriate data structure for a given scenario, optimizing time and space complexities. Choosing the right data structure allows candidates to implement solutions within the time constraints and enables interviewers to assess the interviewee’s technical competency and problem-solving approach.

Educative’s data structures courses for coding interviews, like this one, available in various programming languages, are helpful for revising the data structures concepts. These courses provide practical examples and exercises that reinforce learning. Knowing the strengths and limitations of different data structures enables us to choose the right one for our problems, ensuring that our algorithms perform optimally and handle different edge cases effectively.

Diverse data structure interview questions#

During the initial rounds of interviews, interviewers usually prefer coding questions related to basic data structures. This approach helps break the ice and allows interviewers to assess the candidate’s understanding of core concepts and problem-solving skills. Interviewers typically start with arrays, linked lists, stacks, queues, and hash maps. Some of the common interview problems related to these data structures are listed below:

Data Structure	Problem	Problem Statement
Array	Sum of Three Values	Given an array of integers, `nums`, and an integer value, `target`, determine if there are any three integers in `nums` whose sum is equal to the `target`.
Array	Container With the Most Water	Given an integer array `height` of length 𝑛, there are 𝑛 vertical lines drawn such that the two endpoints of the 𝑖^𝑡ℎ line are (𝑖,0) and (𝑖, `height[i]`). Find two lines from the input array that, together with the x-axis, form a container that holds as much water as possible. Return the maximum amount of water a container can store.
Array	Majority Element	Given an array, `nums`, having 𝑛 integers, return the majority element. An element will be considered a majority element if it occurs more than ⌊𝑛/2⌋ times in the array.
String	Longest Palindromic Substring	Given a string `s`, return the longest palindromic substring in `s`.
Linked list	Remove nth Node from End of List	Given a singly linked list, remove the 𝑛𝑡ℎ node from the end of the list and return its head.
Linked list	Reorder List	Given the head of a singly linked list, reorder it as if it were folded on itself.
Stack	Largest Rectangle in Histogram	Given an array of integers, `heights`, that represents the heights of bars in a histogram, find the area of the largest rectangle in the histogram, where the rectangle has a constant width of 1 unit for each bar.
Hash maps	Next Greater Element	Given the two distinct integer arrays, `nums1` and `nums2`, where `nums1` is a subset of `nums2`, find all the next greater elements for `nums1` values in the corresponding places of `nums2`.

As candidates advance to the next interview rounds, the difficulty of the coding questions increases. Interviewers move from basic to intermediate and, ultimately, to advanced data structures. This progression allows recruiters to evaluate a candidate’s ability to handle more complex problems and proficiency with sophisticated data structures. Candidates may encounter challenges involving balanced trees, heaps, graphs, and advanced structures such as tries. The following list of problems provides an overview of the diversity and complexity of the interview questions typically asked during the later rounds at MAANG.

Data Structure	Problem	Problem Statement
Tree	Lowest Common Ancestor in a Binary Tree	Given the root node of a binary tree with 𝑛n nodes, find the lowest common ancestor of two of its nodes, `p` and `q`.
Tree	Word Ladder	Given two words, `src` and `dest`, and a list `words`, return the number of words in the shortest transformation sequence from `src` to `dest`. If no such sequence can be formed, return 0.
Heap	Find Median from a Data Stream	Create a data structure that can store a list of integers that can change in size over time and find the median from this dynamically growing list in constant time, $O(1)$ .
Graph	Course Schedule	There are a total of `num_courses` courses you have to take. The courses are labeled from `0` to `num_courses - 1`. You are also given a `prerequisites` array, where `prerequisites[i] = [a[i], b[i]]` indicates that you must take course `b[i]` first if you want to take course `a[i]`. For example, the pair [1,0] indicates that to take course 1, you have to first take course 0. Return TRUE if all of the courses can be finished. Otherwise, return FALSE.
Trie	Search Suggestions System	Given an array of strings called `products` and a word to search, design a system that when each character of the searched word is typed, suggests at most three product names from `products`. Suggested products should share a common prefix with the searched word. If more than three products exist with a common prefix, return the three product names that appear first in lexicographical order. Return the suggested products, which will be a list of lists after each character of the searched word is typed.

Choosing the right data structure#

Almost every problem asked in the interview involves the use of data structures. Choosing the right data structure can significantly improve the performance of an algorithm, optimizing its time and space complexity. For example:

Arrays provide random access in constant time but tend to be inefficient for dynamic data where frequent insertions or deletions are needed.
Linked lists are introduced to handle dynamic data at the expense of access times.
Trees offer good search time by keeping the data in an order based on certain criteria. For example, binary search trees maintain the data in sorted order.
Hash maps offer constant-time search operations by keeping the data in key-value pairs using a hash function.

Consider there’s a continuous stream of integers, and the task is to find the median of the numbers at any point in time in $O(1)$ . The naive approach to solving this problem is to sort the list every time a new number is inserted. This would result in $O(n \log n)$ time complexity for each insertion. Its optimized solution is to maintain two heaps: a max-heap to store the lower half of the numbers and a min-heap for the upper half. For each new number, add it to the max-heap if it’s smaller than or equal to the top of the max-heap. Otherwise, add it to the min-heap. The complexity of adding an element to a heap is $O(\log n)$ . The median can be calculated using the top elements of the two heaps in constant time.

from heapq import *
class MedianOfStream:
    def __init__(self):
        self.max_heap_for_smallnum = []
        self.min_heap_for_largenum = []
    def insert_num(self, num):
        if not self.max_heap_for_smallnum or -self.max_heap_for_smallnum[0] >= num:
            heappush(self.max_heap_for_smallnum, -num)
        else:
            heappush(self.min_heap_for_largenum, num)
        if len(self.max_heap_for_smallnum) > len(self.min_heap_for_largenum) + 1:
            heappush(self.min_heap_for_largenum, -heappop(self.max_heap_for_smallnum))
        elif len(self.max_heap_for_smallnum) < len(self.min_heap_for_largenum):
            heappush(self.max_heap_for_smallnum, -heappop(self.min_heap_for_largenum))
    def find_median(self):
        if len(self.max_heap_for_smallnum) == len(self.min_heap_for_largenum):
            # we have even number of elements, take the average of middle two elements
            # we divide both numbers by 2.0 to ensure we add two floating point numbers
            return -self.max_heap_for_smallnum[0] / 2.0 + self.min_heap_for_largenum[0] / 2.0
        # because max-heap will have one more element than the min-heap
        return -self.max_heap_for_smallnum[0] / 1.0
# Driver code
def main():
    median_num = MedianOfStream()
    nums = [35, 22, 30, 25, 1]
    numlist = []
    x = 1
    for i in nums:
        numlist.append(i)
        print(x, ".\tData stream: ", numlist, sep="")
        median_num.insert_num(i)
        print("\tThe median for the given numbers is: " +
              str(median_num.find_median()), sep="")
        print(100*"-"+"\n")
        x += 1
if __name__ == "__main__":
    main()

Preparing for the algorithms#

When preparing for coding interviews, it is important to develop key skills that optimize solutions. An efficient algorithm uses less time and space to solve the problem, which is important for handling large datasets and complex architectures. Because of this, interviewers strongly emphasize algorithmic efficiency to see if you can devise solutions that work better within the time and space constraints. To help you build these essential skills, Algorithms for coding interviews in Java offers a great starting point for preparing algorithms for MAANG interviews.

There are often multiple ways to solve a coding problem, but not all solutions are efficient enough for practical implementation. Next, we’ll discuss two key strategies that help you excel in your coding interviews at MAANG: optimized algorithms and algorithmic paradigms.

Optimized algorithms#

Given a coding problem, its naive algorithm is the straightforward solution to solve it without taking care of efficiency. On the other hand, the main focus of an optimized algorithm is to provide a correct solution while ensuring efficiency. An optimized algorithm gives similar correct results as the naive algorithm but with minimal time and space complexity.

One of the effective strategies for optimizing the algorithms is implementing suitable coding patterns. Coding patterns are reusable templates or approaches to solve similar programming problems. Educative’s Grokking the Coding Interview Patterns course series is a valuable resource for learning and practicing coding patterns.

Let’s review an example in which we discuss three different strategies for solving the same problem and see how applying optimized approaches improves an algorithm’s efficiency.

Example: The Two Sum problem#

Given an array of integers, nums, and an integer, target, return indexes of the two numbers such that they add up to target.

What would the time and space complexities of an optimized solution for the Two Sum problem be?

def two_sum(arr, target):
    n = len(arr)
    for i in range(n):
        for j in range(i + 1, n):
            if arr[i] + arr[j] == target:
                return [i, j]
    
# Driver code
def main():
    arrs = [[3, 7, 1, 2, 8, 4, 5],
            [-1, 2, 1, -4, 5, -3],
            [2, 3, 4, 1, 7, 9],
            [1, -1, 0],
            [5, 4, 2, 7, 6, 0, -8]]
    targets = [3, -1, 12, 0, 8]
    for i in range(len(arrs)):
        print(i + 1, ".\tInput array: ", arrs[i], sep="")
        print("\tTarget: ", targets[i], sep="")
        print("\tSolution: ", two_sum(arrs[i], targets[i]), sep="")
        print("-"*100)
if __name__ == '__main__':
    main()

Two Sum: Naive solution

def binary_search(arr, target, start):
    left, right = start, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1
def two_sum(arr, target):
    arr_with_indices = [(num, i) for i, num in enumerate(arr)]
    arr_with_indices.sort()
    
    for i in range(len(arr_with_indices)):
        complement = target - arr_with_indices[i][0]
        temp_arr = [num for num, _ in arr_with_indices]
        j = binary_search(temp_arr, complement, i + 1)
        if j != -1:
            return [arr_with_indices[i][1], arr_with_indices[j][1]]
    
# Driver code
def main():
    arrs = [[3, 7, 1, 2, 8, 4, 5],
            [-1, 2, 1, -4, 5, -3],
            [2, 3, 4, 1, 7, 9],
            [1, -1, 0],
            [5, 4, 2, 7, 6, 0, -8]]
    targets = [3, -1, 12, 0, 8]
    for i in range(len(arrs)):
        print(i + 1, ".\tInput array: ", arrs[i], sep="")
        print("\tTarget: ", targets[i], sep="")
        print("\tSolution: ", two_sum(arrs[i], targets[i]), sep="")
        print("-"*100)
if __name__ == '__main__':
    main()

Two Sum: Improved solution

def two_sum(arr, target):
    hashmap = {}
    for i in range(len(arr)):
        complement = target - arr[i]
        if complement in hashmap:
            return [i, hashmap[complement]]
        hashmap[arr[i]] = i
    
# Driver code
def main():
    arrs = [[3, 7, 1, 2, 8, 4, 5],
            [-1, 2, 1, -4, 5, -3],
            [2, 3, 4, 1, 7, 9],
            [1, -1, 0],
            [5, 4, 2, 7, 6, 0, -8]]
    targets = [3, -1, 12, 0, 8]
    for i in range(len(arrs)):
        print(i + 1, ".\tInput array: ", arrs[i], sep="")
        print("\tTarget: ", targets[i], sep="")
        print("\tSolution: ", two_sum(arrs[i], targets[i]), sep="")
        print("-"*100)
if __name__ == '__main__':
    main()

Two sum: Optimized solution

Algorithmic paradigms#

Algorithmic paradigms are fundamental approaches used to solve a wide range of complex problems in a structured and effective manner. They empower algorithms to handle large test cases that would otherwise fail using naive algorithms. One of the fundamental principles of algorithmic paradigms is to divide a complex problem into smaller sub-problems.

Imagine you’re in a coding interview, and the interviewer posed a complex coding problem. You’re struggling to find an optimal solution. A good starting point is to check if any of the following algorithmic paradigms can be applied to the problem:

Divide and conquer: Break a problem into smaller subproblems, solve them independently, and combine their solutions for the final result.
Greedy algorithms: Find a locally optimal solution at each step to find a global optimal solution.
Dynamic programming: Solve a problem by combining the solutions of overlapping subproblems to avoid redundant calculations. A dynamic programming interview prep course can be very helpful when preparing to apply this algorithmic paradigm.

Practice makes perfect#

The coding interview journey at MAANG varies—starting from different number of rounds to varying duration of each round. For example, Meta has two main rounds in its interview process: the technical screen and the full-loop interview process. The first includes a 35-minute coding session, whereas the latter includes two 45-minute coding sessions each. Similarly, Google’s on-site interview includes two coding interviews, each lasting 45 minutes.

To ensure you are well-prepared for your upcoming MAANG interview, you need extensive practice to clear the interview with flying colors. To accomplish this, LeetCode and other competitive programming platforms, such as HackerRank, are incredibly valuable. They offer a vast library of coding problems categorized by difficulty and topic, allowing candidates to practice various questions similar to real interviews. You can filter problems based on specific companies to focus on MAANG interview questions. The next section presents a curated list of common coding interview questions asked at MAANG companies.

Common MAANG coding interview questions#

Before appearing for a coding interview at any company, it’s necessary to practice the frequently or recently asked coding problems specific to that company and understand the underlying patterns or strategies behind solving those problems. Below, we present the common coding problems at the five MAANG companies.

Mock interviews#

One of the most beneficial strategies for preparing for an interview is attempting mock interviews. This is because it’s the most effective way to put your theoretical knowledge to the test in a real-world scenario. You can learn more about the types and importance of mock interviews in this blog: Mock interviews for software developers: Which options are best?

Educative’s mock interviews are a game changer when it comes to mock interviews. They offer 40+ coding mock interviews covering data structures, problem-solving, various coding patterns, and dynamic programming patterns. These mock interviews are conducted for different experience levels and programming languages. In the end, detailed feedback is provided, highlighting your strengths and weaknesses and helping you refine your interview preparation.

Conclusion#

Data structures and algorithms interviews at MAANG are challenging but not impossible. Revisiting your data structures knowledge helps you choose the right data structure in your coding interviews and also helps you implement the best solution in your real-world applications. Additionally, optimizing algorithms and applying algorithmic paradigms enhance the performance of your solutions and enable you to build scalable solutions for complex architectures. Consistency is key—keep practicing different data structures and algorithm problems to master these skills. Mock interviews are incredibly handy in this regard.

Ready to ace your MAANG interview? Start by exploring the following skill paths specifically curated to meet the MAANG interview prep requirements.

Frequently Asked Questions

How do I choose the right programming language for MAANG interviews?

The best language is the one you’re most comfortable with. Common choices include Python, Java, and C++. Focus on mastering data structures and algorithms in your preferred language.

Are mock interviews helpful for MAANG preparation?

Absolutely! Mock interviews simulate real interview scenarios, helping you get comfortable with the format, improve your problem-solving speed, and gain confidence.

How many coding problems should I solve to prepare for a MAANG interview?

Aim to solve at least 150–200 problems covering all major data structures and algorithms topics. Focus on quality over quantity and understand the underlying patterns and solutions.

Is solving problems on LeetCode enough for MAANG interviews?

While LeetCode is a great resource, supplement your preparation with mock interviews, review system design concepts, and study behavioral interview questions to cover all aspects.

What resources are best for preparing for MAANG interviews?

Use platforms like LeetCode, HackerRank, and GeeksforGeeks for coding problems, and read books like “Cracking the Coding Interview” and “Elements of Programming Interviews.”

Can I prepare for a data structures interview in one month?

It’s possible but challenging. Dedicate yourself to full-time preparation, focusing on key topics and problem-solving strategies. Use high-quality resources and practice consistently.

Written By:

Muhammad Hamza

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Data Structure	Solution	The Time Complexity of Adding a New Integer	Space Complexity
None	Sort the list every time a new number is inserted.	O(nlogn)	O(n)
Heaps	Maintain two heaps: a max-heap to store the lower half of the numbers and a min-heap for the upper half. Add the new number to the max-heap if it’s smaller than or equal to its top. Otherwise, add it to the min-heap. The median can be calculated using the top elements of the two heaps.	O(logn)	O(n)

Approach	Solution	Time Complexity	Space Complexity
Naive	Check all pairs of numbers to see if they add up to the target sum.	O(n²)	O(1)
Improved	Sort the array and use binary search to find the complement of each element.	O(nlogn)	O(1)
Optimized	Iterate and insert elements into a hash map. While iterating, check if the complement of the current element already exists in the hash map. If yes, return the indexes of the two elements.	O(n)	O(n)

Meta	Amazon	Apple	Netflix	Google
Vertical Order Traversal of a Binary Tree	Two Sum	Two Sum	First Missing Positive	Two Sum
Minimum Remove to Make Valid Parentheses	LRU Cache	Longest Substring without Repeating Characters	Merge Intervals	Logger Rate Limiter
Kth Largest Element in an Array	Longest Substring without Repeating Characters	Product of Array Except Self	Top K Frequent Elements	Merge Sorted Array
Valid Palindrome II	Merge Sorted Array	Best Time to Buy and Sell Stock	Random Pick with Weight	Longest Consecutive Sequence
Basic Calculator II	Trapping Rain Water	LRU Cache	Flatten Nested List Iterator	Maximal rectangle
Random Pick with Weight	Best Time to Buy and Sell Stock	Number of Islands	Insert Delete GetRandom O(1)	Number of Islands
Pow(x, n)	Number of Islands	Longest Common Prefix	Logger Rate Limiter	Trapping Rain Water
Nested List Weight Sum	Group Anagrams	Group Anagrams	LRU Cache	Median of Two Sorted Arrays
Merge Sorted Array	Add Two Numbers	Integer to English Words	Meeting Rooms II	Range Module
Subarray Sum Equals K	Valid Parentheses	Merge Sorted Array	Time-Based Key-Value Store	Climbing Stairs