Data Structures and Algorithms
- Introduction to Data Structures and Algorithms
- Time and Space Complexity Analysis
- Big-O, Big-Theta, and Big-Omega Notations
- Recursion and Backtracking
- Divide and Conquer Algorithm
- Dynamic Programming: Memoization vs. Tabulation
- Greedy Algorithms and Their Use Cases
- Understanding Arrays: Types and Operations
- Linear Search vs. Binary Search
- Sorting Algorithms: Bubble, Insertion, Selection, and Merge Sort
- QuickSort: Explanation and Implementation
- Heap Sort and Its Applications
- Counting Sort, Radix Sort, and Bucket Sort
- Hashing Techniques: Hash Tables and Collisions
- Open Addressing vs. Separate Chaining in Hashing
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
DSA Interview Questions
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
Data Structures and Algorithms
- Introduction to Data Structures and Algorithms
- Time and Space Complexity Analysis
- Big-O, Big-Theta, and Big-Omega Notations
- Recursion and Backtracking
- Divide and Conquer Algorithm
- Dynamic Programming: Memoization vs. Tabulation
- Greedy Algorithms and Their Use Cases
- Understanding Arrays: Types and Operations
- Linear Search vs. Binary Search
- Sorting Algorithms: Bubble, Insertion, Selection, and Merge Sort
- QuickSort: Explanation and Implementation
- Heap Sort and Its Applications
- Counting Sort, Radix Sort, and Bucket Sort
- Hashing Techniques: Hash Tables and Collisions
- Open Addressing vs. Separate Chaining in Hashing
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
DSA Interview Questions
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
Top Microsoft Interview Questions for Software Engineers
Landing a software engineering role at Microsoft is a dream for many tech professionals, given the company’s innovative culture and impact on global technology. Whether you’re aiming for an entry-level SDE position or a senior role, preparing for the interview means diving deep into technical challenges, system architecture, and real-world problem-solving. To stay ahead and access free course updates tailored to tech interviews, sign up for our newsletter here—it’s a quick way to get resources that can boost your prep.
In this comprehensive guide, we’ll break down the top questions actually asked in Microsoft interviews, based on recent reports from 2024 and early 2025. We’ll cover coding, system design, and behavioral aspects, with in-depth explanations, solutions, and tips to help you shine. Drawing from sources like LeetCode discussions, Glassdoor reviews, and expert platforms, this post aims to provide actionable insights that go beyond basics.
The Microsoft Interview Process for Software Engineers
Microsoft’s interview process is rigorous but fair, typically spanning 4-8 weeks and focusing on problem-solving, collaboration, and cultural fit. It starts with a resume screen, often boosted by referrals (about 13% of candidates get in via this route). Next comes a recruiter phone screen to gauge your interest and fit.
The technical phases include:
- Online Assessment (OA): 60-90 minutes on platforms like Codility, with 2-3 coding problems on data structures and algorithms (DS&A).
- Technical Phone Screen: 45-60 minutes, mixing coding and behavioral questions.
- Onsite/Virtual Loop: 4-5 one-hour interviews, covering coding (3-4 rounds), system design (1-2 for mid-senior levels), and behavioral elements throughout.
- As Appropriate (AA) Round: A final chat with a senior leader if you’re a strong candidate.
Expect questions in your preferred language, often on collaborative editors without syntax help. Interviewers evaluate on competencies like analytical ability and creativity, grading with “hire/no hire” recommendations. In 2025, with AI tools like Copilot gaining traction, questions may touch on integrating emerging tech.
Key Skills Tested in Microsoft Interviews
Microsoft seeks engineers who excel in DS&A, system scalability, and soft skills. Core areas include:
- Data Structures: Arrays, linked lists, trees, graphs—expect to manipulate them efficiently.
- Algorithms: Sorting, searching, dynamic programming (DP), with time/space complexity analysis.
- System Design: High-level architecture for large-scale systems, focusing on trade-offs.
- Behavioral: Alignment with Microsoft’s growth mindset, emphasizing learning from failures and teamwork.
Statistics show coding questions dominate (70-80% of technical rounds), with arrays/strings at 36%, linked lists at 29%, and graphs/trees at 20%. For deeper prep on DS&A, explore our DSA course to build a strong foundation.
Top Coding Interview Questions
Coding rounds test your ability to solve problems under time constraints. We’ve compiled 25 real questions reported in recent interviews (2024-2025), categorized for ease. Each includes an in-depth explanation, approach, sample code (in Python for clarity), and complexity analysis. These draw from LeetCode-tagged problems frequently asked at Microsoft.
Arrays and Strings
Find the missing number in a given array Given an array of positive integers from 1 to n with one missing, find it. This tests math and efficiency. Approach: Use the formula for the sum of first n naturals: n*(n+1)/2. Subtract the array sum. Handles duplicates edge cases. Code:
def missingNumber(nums):
n = len(nums)
expected = n * (n + 1) // 2
return expected - sum(nums)
Complexity: Time O(n), Space O(1). Alternative: XOR for overflow safety.
Determine if the sum of two integers equals a given value Classic two-sum problem. Approach: Hash map to store seen numbers; check if target – current exists. Code:
def twoSum(nums, target):
seen = {}
for i, num in enumerate(nums):
complement = target - num
if complement in seen:
return [seen[complement], i]
seen[num] = i
Complexity: Time O(n), Space O(n).
Set columns and rows as zeroes in a matrix If an element is zero, zero out its row and column. Approach: Use first row/column as flags to avoid extra space. Code: (Detailed in full post for brevity—scan matrix, mark flags, then update.) Complexity: Time O(m*n), Space O(1).
Reverse words in a sentence Reverse word order without extra space. Approach: Reverse entire string, then reverse each word. Code:
def reverseWords(s):
words = s.split()
return ' '.join(words[::-1])
Complexity: Time O(n), Space O(n).
- Find all palindrome substrings Count non-single palindromic substrings. Approach: Expand around centers for odd/even lengths. Code: (Expand function to check palindromes.) Complexity: Time O(n^2), Space O(1).
Linked Lists
- Add two integers represented as linked lists Digits in reverse order. Approach: Traverse, add with carry, build new list. Code: (Dummy node for result.) Complexity: Time O(max(m,n)), Space O(max(m,n)).
- Copy linked list with arbitrary pointer Deep copy with random pointers. Approach: Interleave copies, set randoms, separate. O(1) space variant. Code: (Three passes: weave, random, unwind.) Complexity: Time O(n), Space O(1).
Merge two sorted linked lists Into one sorted list. Approach: Recursive or iterative merge. Code:
def mergeTwoLists(l1, l2):
dummy = ListNode()
tail = dummy
while l1 and l2:
if l1.val < l2.val:
tail.next = l1
l1 = l1.next
else:
tail.next = l2
l2 = l2.next
tail = tail.next
tail.next = l1 or l2
return dummy.next
Complexity: Time O(m+n), Space O(1).
- Reverse nodes in k-group Reverse every k nodes. Approach: Reverse sublists, link them. Complexity: Time O(n), Space O(1).
- Swap adjacent nodes Pairwise swap. Approach: Iterative pointer swaps. Complexity: Time O(n), Space O(1).
Trees and Graphs
- Level order traversal of binary tree Print levels. Approach: BFS with queue. Code: (Queue, process size per level.) Complexity: Time O(n), Space O(n).
- Check if binary tree is symmetric Mirror check. Approach: Recursive compare left/right. Complexity: Time O(n), Space O(h).
- Construct binary tree from preorder and inorder Build tree. Approach: Inorder for root index, recursive build. Complexity: Time O(n), Space O(n).
- Binary Tree Path Sum Root-to-leaf sum equals target. Approach: DFS subtract sum. Complexity: Time O(n), Space O(h).
- Clone a directed graph Deep copy. Approach: DFS/BFS with map. Complexity: Time O(V+E), Space O(V).
Dynamic Programming and Others
Find maximum single sell profit Stock prices for max profit. Approach: Track min price, max profit. Code:
def maxProfit(prices):
min_price = float('inf')
max_profit = 0
for price in prices:
min_price = min(min_price, price)
max_profit = max(max_profit, price - min_price)
return max_profit
 Complexity: Time O(n), Space O(1).
- Length of longest subsequence Increasing then decreasing. Approach: Two DP arrays for inc/dec. Complexity: Time O(n^2), Space O(n).
- Find kth permutation Of n elements. Approach: Factorial positioning. Complexity: Time O(n), Space O(n).
- Regular expression matching With . and . Approach: DP table for matches. Complexity: Time O(mn), Space O(m*n).
- Solve Sudoku Fill 9×9 grid. Approach: Backtracking, validate row/col/box. Complexity: Time O(9^{n}), Space O(1).
- Buddy Strings Swap to match. Approach: Count freq, check swaps. Complexity: Time O(n), Space O(1).
- Can Place Flowers No adjacent. Approach: Greedy placement. Complexity: Time O(n), Space O(1).
- Min Cost Climbing Stairs DP costs. Approach: DP min to each step. Complexity: Time O(n), Space O(1).
- Intersection of Two Arrays Unique common. Approach: Sets intersect. Complexity: Time O(m+n), Space O(min(m,n)).
- Nth Digit In infinite sequence. Approach: Calculate digit groups. Complexity: Time O(log n), Space O(1).
For more practice on these, our crash course offers quick drills.
System Design Interview Questions
System design rounds assess scalability and trade-offs, especially for L61+ roles. Expect 45-60 minutes to discuss high-level designs.
- Design a URL shortener Like TinyURL. Key: Hashing for keys, database for mapping, handle collisions. Trade-offs: Redis for cache, SQL for persistence. Scale to 100M users: Sharding.

- Design a social media feed Like Twitter. Components: User DB, post storage (Cassandra), ranking algo (ML for relevance). Handle real-time: WebSockets.
- Design a parking lot system OOP design: Classes for spots, vehicles. Scale: Multi-level, sensors for availability.
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- Design an autocomplete system Like search suggestions. Trie for prefixes, ranked by frequency. Cache top queries.
- Design a file storage system Like Dropbox. Chunks, metadata DB, sync with diffs. Security: Encryption.
- Design a ride-sharing app Like Uber. Geospatial indexing (quadtree), matching algo, ETA calc.
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For system design mastery, check our Master DSA, Web Dev, System Design course.
Behavioral Interview Questions
Behavioral questions probe your experiences, often using STAR (Situation, Task, Action, Result). Prepare stories from your career.
- Tell me about a time you made a mistake. Focus on learning: E.g., “I overlooked a edge case in code, causing a bug. I added tests and reviewed processes.”
- Why Microsoft? Tie to products: “I’m drawn to Azure’s cloud innovation and Microsoft’s commitment to AI ethics.”
- Describe a conflict with a colleague. Resolution: “Differing on architecture; we discussed pros/cons and compromised.”
- Favorite Microsoft product and improvement? E.g., “Teams—add AI for better meeting summaries.”
- How do you handle a challenge? “Prioritize, break down, seek input if needed.”
- Tell me about a time you worked in a team. “Collaborated on a microservice migration, dividing tasks for efficiency.”
Preparation Strategies and Resources
To ace Microsoft interviews:
- Practice on LeetCode (focus Microsoft tag, 500+ problems).
- Mock interviews: Simulate with peers.
- Study Azure basics for cloud roles.
- For web dev angles, our Web Development course covers full-stack prep.
- Data science overlaps? Explore Data Science course.
Aim for 100-200 problems solved. Recent stats: 75% of hires practiced behavioral stories.
Conclusion
Preparing for Microsoft interviews requires blending technical depth with storytelling. By mastering these 36+ questions, you’ll be well-equipped. Start practicing today—what’s one question you’ll tackle first? For more tailored guidance, join our community and dive into resources like our crash course.
FAQs
What are common Microsoft coding interview questions for SDE roles?
Common ones include two-sum, linked list reversal, and matrix zeroing, focusing on arrays, strings, and trees for efficient solutions.
How to prepare for Microsoft system design interviews in 2025?
Expect queries on mistakes, conflicts, teamwork, and why Microsoft, using STAR method to highlight growth mindset.
Top tips for Microsoft software engineer interview prep?
Practice LeetCode, mock interviews, study Azure, and prepare stories—aim for 100+ problems and behavioral examples.
Are Microsoft interviews harder for senior SDE positions?
Yes, with more system design emphasis on leadership, scalability, and real-world trade-offs beyond junior coding focus.

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