Top 36 Microsoft Internship Interview Questions and Answers

Did you know that over the past five years, companies have been actively seeking more interns? According to a recent Internshala survey, internship applications in India increased by 80%, with 2.2 million submissions in 2018, compared to 1.27 million the previous year. With this rising competition, securing a coveted position, such as a Microsoft internship, requires thorough preparation. Microsoft internships are known for offering hands-on experience with cutting-edge technology, making them highly sought after by aspiring tech professionals. In this blog, we will walk you through the crucial Microsoft internship interview questions and answers to help you stand out and ace your interview.

Tech-Based Microsoft Internship Interview Questions and Answers

Let us look at some of the key technical questions you may encounter in a Microsoft internship interview. These Microsoft internship interview questions that will be asked to you will assess your problem-solving and analytical abilities.

Q1. What is a Binary Search, and how does it work?

Sample Answer: Binary search is a quick method to find an element in a sorted array. It works by dividing the array in half repeatedly. If the target value is smaller than the middle element, I search the left half; otherwise, I search the right. This process continues until the value is found or the array is exhausted. The time complexity is O(log n).

Q2. Explain how you would reverse a linked list iteratively.

Sample Answer: To reverse a linked list, I use three pointers: prev, curr, and next. I traverse the list starting from the head. For each node, I change curr to point to prev, then move next to continue the traversal. Finally, I return prev as the new head of the reversed list. The time complexity is O(n).

Q3. How would you check if a number is a power of two?

Sample Answer: A number is a power of two if it has only one bit set in its binary representation. I check this using the bitwise expression n & (n – 1) == 0 and ensure n is greater than zero. This approach gives an efficient O(1) time complexity.

Q4. How do you detect a cycle in a linked list?

Sample Answer: To detect a cycle, I use Floyd’s Cycle Detection Algorithm, also known as the two-pointer method. One pointer (slow) moves one step at a time, while the other (fast) moves two steps. If the two pointers meet, a cycle exists. If fast reaches the end of the list, no cycle is present.

Q5. What is the difference between Merge Sort and Quick Sort?

Sample Answer: Merge sort divides the array into two halves, recursively sorts them, and merges the results. It has a time complexity of O(n log n) in all cases. Quick sort, while faster on average, can degrade to O(n²) in the worst case when the pivot selection is poor. Merge sort uses more space, whereas quicksort is more space-efficient.

Q6. Explain the Sliding Window technique with an example.

Sample Answer: The sliding window technique helps solve problems involving subarrays or substrings. I keep a window of elements, adjusting it as needed while traversing the array. For instance, to find the maximum sum of a subarray of length k, I sum the first k elements, then slide the window, adjusting the sum at each step. The time complexity is O(n).

Q7. How would you find the maximum sum subarray?

Sample Answer: To find the maximum sum subarray, I use Kadane’s Algorithm. I initialize a variable to store the maximum sum and iterate through the array, deciding whether to extend the current subarray or start a new one with each element. This method runs in O(n) time, ensuring optimal performance.

Q8. How would you determine the lowest common ancestor in a binary search tree?

Sample Answer: To find the lowest common ancestor (LCA), I start at the root and compare the values of the two nodes. If both nodes are smaller than the current node, I move to the left subtree; if both are larger, I move to the right. When the values split, the current node is the LCA.

Q9. Can you explain how you would implement an LRU cache?

Sample Answer: I would implement an LRU (Least Recently Used) cache using a hash map and a doubly linked list. The hash map provides O(1) time complexity for access, while the doubly linked list maintains the order of usage. When the cache is full, I remove the least recently used item, which is the tail node of the linked list.

Q10. How would you detect if two strings are anagrams?

Sample Answer: To check if two strings are anagrams, I first ensure they have the same length. Then, I count the frequency of each character in both strings. If the frequency matches for all characters, the strings are anagrams. This approach runs in O(n) time, where n is the length of the strings.

Q11. How do you merge two sorted linked lists?

Sample Answer: To merge two sorted linked lists, I maintain a dummy node to simplify pointer manipulation. I compare the head nodes of both lists and append the smaller node to the result list. I repeat this process until one list is exhausted, then append the remaining nodes of the other list. This approach runs in O(n) time.

Q12. How would you solve the problem of finding the kth smallest element in an unsorted array?

Sample Answer: I would use the Quickselect algorithm to solve this problem. Quickselect partitions the array based on a pivot, similar to Quicksort, but focuses only on finding the desired kth element without fully sorting the array. The average time complexity is O(n), making it more efficient than sorting.

Q13. What is a heap, and how would you implement a min-heap?

Sample Answer: A min-heap is a binary tree where each parent node is smaller than its children. I would implement a min-heap using an array where the parent of a node at index i is at index (i-1)/2, and the children are at indices 2*i+1 and 2*i+2. Operations like inserting and removing follow heapification rules to maintain the heap property.

Q14. How would you check if a binary tree is balanced?

Sample Answer: To check if a binary tree is balanced, I recursively calculate the height of the left and right subtrees for each node. If the difference between their heights exceeds one, the tree is not balanced. I return true only if every subtree satisfies this condition. This approach runs in O(n) time.

Q15. How do you solve the problem of trapping rainwater between bars?

Sample Answer: To solve the trapping rainwater problem, I use two arrays to store the maximum heights from the left and right of each bar. For each bar, I calculate the trapped water as the difference between the minimum of these two heights and the bar’s height. This approach runs in O(n) time and uses O(n) space.

Q16. Explain Depth First Search (DFS) and its application in graph traversal.

Sample Answer: Depth-First Search (DFS) is a graph exploration method that goes down each path as far as it can before stepping back to try a different path. I implement DFS using either recursion or a stack. It’s useful in many applications, such as detecting cycles in a graph, topological sorting, and finding connected components. The time complexity of DFS is O(V + E), where V represents the number of vertices and E is the number of edges in the graph.

Q17. How would you solve the N-Queens problem using backtracking?

Sample Answer: The N-Queens problem involves placing N queens on an NxN chessboard so that no two queens threaten each other. I use backtracking to place queens row by row, checking if each placement is safe. If a conflict occurs, I backtrack and try a different position. The algorithm runs in O(N!) time due to the recursive nature of the solution.

Q18. How do you detect a palindrome in a linked list?

Sample Answer:  To detect a palindrome in a linked list, I use two steps. First, I find the middle of the list using the fast and slow pointer method. Then, I reverse the second half of the list. Finally, I compare the first and second halves. If they match, the linked list is a palindrome. This approach runs in O(n) time.

Q19.  How would you solve the problem of finding a cycle in a directed graph?

Sample Answer: To detect a cycle in a directed graph, I use Depth First Search (DFS) with recursion. During DFS, I maintain two arrays: visited and recStack (recursive stack). For each node, I mark it as visited and add it to the recursive stack. If I encounter a node that is already in the recursive stack, it indicates a cycle. After processing a node, I remove it from the recursive stack. This method has a time complexity of O(V + E), where V is the number of vertices and E is the number of edges, ensuring optimal performance for large graphs.

Q20. How would you solve the problem of finding the median in a stream of integers?

Sample Answer: To efficiently find the median in a stream of integers, I use two heaps: a max-heap for the lower half of the elements and a min-heap for the upper half. The max-heap stores the smaller half of the numbers, and the min-heap stores the larger half. For every new element, I add it to one of the heaps, ensuring that the heaps remain balanced (i.e., their size differs by at most one). If the number of elements is odd, the median is the top of the larger heap; if it’s even, the median is the average of the tops of both heaps. This approach ensures a time complexity of O(log n) for insertion and O(1) for finding the median.

Behavioural and Situation-Based Microsoft Internship Interview Questions and Answers

Let us now explore some behavioural and situation-based questions you might face during your Microsoft internship interview. These Microsoft internship interview questions assess your teamwork, leadership, and decision-making skills.

Q21. Tell me about a time when you had to work under tight deadlines on a project. How did you handle it?

Sample Answer: In my previous internship, I was tasked with delivering a feature for a software application in just a week, which typically took two weeks. I immediately prioritized tasks by breaking them into manageable components. I focused on core functionalities first, ensuring the most critical parts were built and tested. I maintained regular communication with my team, providing updates and requesting feedback. This allowed me to adjust the project quickly based on early inputs. By staying organized and clear on priorities, I successfully completed the project within the deadline without sacrificing quality.

Q22. Describe a situation where you faced a significant challenge while working on a technical problem. How did you approach it?

Sample Answer: During a group project, we encountered a significant bug in our code right before a demo, which caused the application to crash frequently. I took the lead in debugging the issue by isolating the error through systematic testing and breaking down the code into smaller sections to identify the root cause. After pinpointing the problematic area, I refactored the code to handle edge cases more effectively. Throughout the process, I communicated with the team, explaining my steps and ensuring everyone understood the changes. This collaborative approach helped us resolve the issue and deliver a successful demo.

Q23. Can you provide an example of when you had to learn a new technology or skill quickly to complete a project?

Sample Answer: While working on a project that required data visualization, I realized I needed to learn D3.js, a JavaScript library for creating complex charts and graphs, which I hadn’t used before. I dedicated a couple of days to learning the basics, referring to tutorials and documentation. I then applied what I learned to the project by building smaller, testable components before integrating them into the main application. This approach allowed me to gain confidence in the new skill while meeting the project’s deadline. The final visualizations were praised for their clarity and accuracy.

Q24. Have you ever disagreed with a teammate about how to approach a technical problem? How did you resolve it?

Sample Answer: Yes, during a hackathon, a teammate and I disagreed on whether to use a relational or NoSQL database for our project. I preferred relational for its structure, while my teammate advocated for NoSQL due to scalability concerns. Instead of arguing, I suggested we analyze the project’s specific needs and constraints. We listed out the pros and cons of each database based on those criteria, and it became clear that scalability was less important in this case. We agreed to go with a relational database, and I made sure to acknowledge my teammate’s valid points to foster collaboration moving forward.

Q25. Tell me about a time you had to balance multiple projects with competing deadlines. How did you manage your time?

Sample Answer: In my final semester, I had to manage multiple academic projects alongside my internship responsibilities. To handle this, I created a detailed schedule, breaking down each project into smaller tasks with specific deadlines. I used a time-blocking technique, dedicating certain hours of the day to different projects and sticking to this routine. Additionally, I regularly reviewed my progress to adjust the schedule when needed, ensuring that no task was neglected. This systematic approach allowed me to complete all projects successfully while maintaining high-quality work.

Q26. Describe a situation where you had to work with a difficult team member. How did you handle the situation?

Sample Answer: In one of my group projects, a team member consistently missed deadlines and did not contribute equally. I approached them privately and asked if there were any issues they were facing. It turned out they were struggling with the technical aspect of the project. I offered to help them understand the task better and proposed redistributing some responsibilities based on each team member’s strengths. By showing empathy and offering support instead of confrontation, I was able to get them more engaged. Our team worked more effectively after that, and the project was completed successfully.

Q27. Have you ever had to deliver bad news to your team or manager? How did you handle it?

Sample Answer: During a software development project, I realized halfway through that a key feature we had planned wasn’t feasible within the given timeframe due to technical limitations. I immediately informed my manager and the team, explaining the challenge and the reason behind it. Instead of just delivering bad news, I came prepared with alternative solutions, including a scaled-down version of the feature that we could implement within the deadline. My manager appreciated the transparency and proactive approach, and we were able to shift our focus to the alternative plan, keeping the project on track.

Q28. Tell me about a time when you received feedback that you didn’t agree with. How did you respond?

Sample Answer: In one of my past internships, I received feedback from a supervisor suggesting that I needed to improve my communication during team meetings. Initially, I disagreed because I felt I was contributing enough. However, instead of dismissing the feedback, I took the time to reflect on it and asked for specific examples of where I could improve. After discussing it with my supervisor, I realized that while I was contributing, I wasn’t always being clear in articulating my ideas. I worked on improving my clarity and became more mindful during future meetings, which helped strengthen my team dynamics.

Q29. Given a binary tree, you are asked to determine its maximum depth. How would you calculate the depth?

Sample Answer: I will use a depth-first search (DFS) approach by recursively finding the maximum depth of the left and right subtrees and adding 1 for the root.

Code:

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right
def max_depth(root):
    if not root:
        return 0
    return max(max_depth(root.left), max_depth(root.right)) + 1

Q30. You are given an array of integers where each element appears twice except for one element that appears only once. How would you find the single element in linear time and constant space?

Sample Answer: I can solve this using bit manipulation (XOR). XOR of all elements will result in a single element since x^x = 0 and x^0 = x.

Code:

def single_number(nums):
    result = 0
    for num in nums:
        result ^= num
    return result

Q31. You’re asked to implement a function that returns the top k frequent elements from an array. How would you approach this problem?

Sample Answer: I can use a hash map to count frequencies and a min-heap to store the top k elements efficiently.

Code:

import heapq
from collections import Counter

def top_k_frequent(nums, k):
    freq_map = Counter(nums)
    return heapq.nlargest(k, freq_map.keys(), key=freq_map.get)

Q32. You have a list of integers, and you are asked to return the product of all the numbers except the one at the current index. You must do this in O(n) time and without using division. How would you solve it?

Sample Answer: I will create two auxiliary arrays; one to store the product of all elements before the current index and another for all elements after it. Once this is done, I will multiply them to get the result.

Code:

def product_except_self(nums):
    n = len(nums)
    left_products = [1] * n
    right_products = [1] * n
    result = [1] * n
    
    for i in range(1, n):
        left_products[i] = left_products[i - 1] * nums[i - 1]
    
    for i in range(n - 2, -1, -1):
        right_products[i] = right_products[i + 1] * nums[i + 1]
    
    for i in range(n):
        result[i] = left_products[i] * right_products[i]
    
    return result

Q33. You are asked to rotate an NxN matrix clockwise at 90 degrees in place. How would you implement this?

Sample Answer: First, I will transpose the matrix by swapping rows with columns. Then, I will reverse each row to achieve the 90-degree rotation.

Code:

def rotate_matrix(matrix):
    n = len(matrix)
    for i in range(n):
        for j in range(i, n):
            matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]
    
    for i in range(n):
        matrix[i].reverse()

Q34. You need to design an algorithm to merge overlapping intervals. For instance, given intervals [[1, 3], [2, 4], [5, 7]], the result should be [[1, 4], [5, 7]]. How would you tackle this?

Sample Answer: I will sort the intervals based on the start time, then iterate through them and merge overlapping intervals by comparing the current interval’s start and the previous interval’s end.

Code:

def merge_intervals(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
        if not merged or merged[-1][1] < interval[0]:
            merged.append(interval)
        else:
            merged[-1][1] = max(merged[-1][1], interval[1])
    return merged

Q35. You’re tasked with writing a function that checks if a string contains all unique characters, but you’re not allowed to use additional data structures. How would you approach it?

Sample Answer: Since no extra data structures are allowed, you can sort the string and then check for any consecutive repeating characters.

Code:

def all_unique_characters(s):
    s = ''.join(sorted(s))  # Sort the string
    for i in range(1, len(s)):
        if s[i] == s[i-1]:
            return False
    return True

Q36. You are given a large sorted array, but it’s mostly made up of zeros with a few ones scattered throughout. How would you efficiently find the position of the first ‘1’ in the array?

Sample Answer: To solve this efficiently, I will use a modified binary search. Since the array is sorted, all zeros come before the ones, so binary search will help quickly locate the first occurrence of ‘1’.

Code:

def find_first_one(arr):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = left + (right - left) // 2
        if arr[mid] == 1 and (mid == 0 or arr[mid - 1] == 0):
            return mid
        elif arr[mid] == 0:
            left = mid + 1
        else:
            right = mid - 1
    return -1  # If no '1' is found

Conclusion

Preparing thoroughly for a Microsoft internship interview is essential to stand out from the competition. By mastering both technical and behavioural Microsoft internship interview questions and answers, you will boost your confidence and improve your chances of success. Focus on understanding key concepts, practising coding problems, and refining your problem-solving skills. Don’t forget that soft skills like teamwork and adaptability are equally important. 
If you’re looking for more tips on landing your dream engineering internship, check out our blog on how to get an internship in engineering for valuable insights and guidance. Best of luck with your Microsoft internship journey!