Top Microsoft DSA Interview Questions and Tips for Success

Preparing for a Microsoft Data Structures and Algorithms (DSA) interview can feel overwhelming, but with the right strategy, you can stand out. Did you know that over 70% of candidates struggle with optimizing their code during technical interviews? To help you prepare, we’ve compiled the top Microsoft DSA interview questions and success strategies. Don’t forget to sign up for free course updates to stay ahead in your preparation!

Understanding Microsoft’s DSA Interview Structure

Microsoft’s DSA interviews test your problem-solving skills, coding efficiency, and ability to handle real-world scenarios. The process typically includes 2–3 coding rounds, each lasting 45–60 minutes.

Types of Questions Asked

Questions range from arrays and strings to advanced graph algorithms. For example:

  • Arrays: Rotate matrices, find subarrays with specific sums.
  • Trees: Validate BSTs, perform vertical traversals.
  • Dynamic Programming: Solve knapsack or longest palindromic substring problems.

Evaluation Criteria

Candidates are graded on:

  1. Problem-Solving Approach: How logically you break down the problem.
  2. Code Efficiency: Optimal time and space complexity (e.g., O(n log n)).
  3. Communication: Explaining your thought process clearly.

Evaluation Aspect

Description

Weightage

Problem-Solving

Logical breakdown and edge cases

40%

Coding Skills

Clean, efficient code

35%

Communication

Clarity in explaining solutions

25%

A Microsoft hiring manager once said, “We look for candidates who ask clarifying questions before jumping into code.”

Essential Tips for Acing the DSA Interview

Practice Smartly

Focus on high-frequency topics like trees, graphs, and DP. Platforms like LeetCode and CodeSignal offer Microsoft-specific question banks.

Master Time Management

  • Spend 10 minutes understanding the problem.
  • Write pseudocode before coding.
  • Allocate 5–10 minutes for testing edge cases.  
 
Essential Tips for Acing the DSA Interview

Top 20 Microsoft DSA Interview Questions

 

Arrays and Strings

1. Rotate a Matrix by 90 Degrees

  • Approach: Layer-by-layer rotation with in-place swaps.

  • Time Complexity: O(n²)

2. Longest Substring Without Repeating Characters

  • Approach: Sliding window with hashmap for indices.

  • Time Complexity: O(n)

3. Two Sum

  • Approach: Hashmap to store complements.

  • Time Complexity: O(n)

4. Maximum Subarray (Kadane’s Algorithm)

  • Approach: Track current and global maxima.

  • Time Complexity: O(n)

5. Merge Intervals

  • Approach: Sort intervals and merge overlapping ones.

  • Time Complexity: O(n log n)

Linked Lists

6. Reverse a Linked List

  • Approach: Iterative three-pointer method.

  • Time Complexity: O(n)

7. Detect Cycle in a Linked List

  • Approach: Floyd’s Tortoise and Hare algorithm.

  • Time Complexity: O(n)

8. Merge Two Sorted Lists

  • Approach: Dummy node for streamlined merging.

  • Time Complexity: O(n + m)

9. Remove Nth Node From End

  • Approach: Two pointers with a gap of n nodes.

  • Time Complexity: O(n)

Trees and Graphs

10. Validate a Binary Search Tree (BST)

  • Approach: In-order traversal with bounds checking.

Time Complexity: O(n)

Trees and Graphs

11. Binary Tree Level Order Traversal

  • Approach: BFS using a queue.
  • Time Complexity: O(n)

12. Lowest Common Ancestor (LCA) in a BST

  • Approach: Exploit BST properties to find split point.
  • Time Complexity: O(h)

13. Clone a Graph (DFS/BFS)

  • Approach: Hashmap to map original and cloned nodes.
  • Time Complexity: O(n)

14. Course Schedule (Topological Sorting)

  • Approach: Detect cycles using Kahn’s algorithm.
  • Time Complexity: O(n + e) 

 

Dynamic Programming (DP)

15. 0/1 Knapsack Problem

  • Approach: DP table with weight-value pairs.
  • Time Complexity: O(nW)

16. Longest Palindromic Subsequence

  • Approach: DP with substring expansion.
  • Time Complexity: O(n²)

17. Climbing Stairs (Fibonacci)

  • Approach: Bottom-up DP with memoization.
  • Time Complexity: O(n)

18. Coin Change Problem

  • Approach: DP array for minimum coins per amount.
  • Time Complexity: O(n × amount)

Hash Tables and Heaps

19. Subarray Sum Equals K

  • Approach: Prefix sum with hashmap.
  • Time Complexity: O(n)

20. Find the Kth Largest Element

  • Approach: Min-heap of size k.
  • Time Complexity: O(n log k)

 

Common Mistakes to Avoid

Ignoring Edge Cases

Forgetting to test empty inputs or large datasets can cost you. For example, always check if a tree is empty before traversal.

Common Mistakes to Avoid

Overcomplicating Solutions

Aim for simplicity. If your code has nested loops, ask: “Can this be done with a hashmap or two pointers?”

Resources for Preparation

Online Platforms

  • LeetCode: Filter questions by Microsoft tag.

  • GeeksforGeeks: Detailed explanations for 500+ DSA problems.

Books

  • Cracking the Coding Interview by Gayle Laakmann McDowell.

  • Introduction to Algorithms by Cormen (for theory).

Comparison of Approaches for Key Problems

Problem

Optimal Approach

Time Complexity

Two Sum

Hashmap

O(n)

Detect Cycle in Linked List

Floyd’s Algorithm

O(n)

Validate BST

In-order Traversal

O(n)

Coin Change

Dynamic Programming

O(n × amount)



FAQ Section

Which topics are most frequent in Microsoft DSA interviews?

 Arrays, strings, trees, and dynamic programming appear in 80% of interviews. Strengthen these areas with our DSA & Web Dev Combined Course.

 Critical! Microsoft evaluates solutions for O(n) or O(n log n) time. Practice optimizing nested loops.

 Balance both. Understand Big-O notation but prioritize writing clean code. Use our Master DSA, Web Dev & System Design for end-to-end prep.

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