Preparing for a Microsoft interview in 2026 can be a challenging yet rewarding experience. Among the most common and critical topics in these interviews are Algorithms and Data Structures (DS). Microsoft’s interview process is known for testing candidates on their problem-solving skills, technical depth, and ability to think critically under pressure. To help you succeed, we’ve compiled the top 15 Algorithms and Data Structures interview questions that you’re likely to face during your Microsoft interview. Along with each question, we’ll guide you on how to answer them and provide a sample solution for better clarity.

These questions cover a broad range of topics and will help you prepare thoroughly for the interview. So let’s get started!

1. Explain the difference between an array and a linked list.

Explain that an array is a collection of elements stored in contiguous memory locations, while a linked list is a collection of nodes where each node contains data and a reference (or link) to the next node in the sequence.

Sample Answer:
The main difference between an array and a linked list is that an array is stored in contiguous memory locations, making it efficient for random access to elements. However, it has a fixed size, meaning resizing it involves copying data to a new array. A linked list, on the other hand, is composed of nodes that each contain data and a reference to the next node. It allows for dynamic resizing, but accessing elements requires traversal from the head node, making it less efficient for random access compared to arrays.

2. What is a stack? How is it different from a queue?

Explain the Last In First Out (LIFO) property of a stack and the First In First Out (FIFO) property of a queue. Discuss their use cases and when each data structure would be useful.

Sample Answer:
A stack is a data structure that follows the LIFO principle, meaning the last element added is the first one to be removed. It supports operations like push (add an element) and pop (remove an element). A queue, on the other hand, follows the FIFO principle, meaning the first element added is the first one to be removed. It supports enqueue (add) and dequeue (remove) operations. Stacks are useful for tasks like undo operations, while queues are often used in breadth-first search (BFS) or job scheduling.

3. Explain the time complexity of accessing an element in an array and a linked list.

Discuss the time complexity of accessing an element at a given index in an array (which is O(1)) versus accessing an element in a linked list (which requires O(n) time for traversal).

Sample Answer:
In an array, accessing an element by index is a constant-time operation, i.e., O(1), because arrays are stored in contiguous memory locations, and the element can be accessed directly. In a linked list, however, accessing an element by index requires traversing the list from the head node to the desired position, which takes O(n) time in the worst case, where n is the number of elements in the list.

4. What is a binary tree? How is it different from a binary search tree?

Explain that a binary tree is a tree data structure in which each node has at most two children. A binary search tree (BST) is a type of binary tree where the left child is smaller than the parent, and the right child is larger than the parent.

Sample Answer:
A binary tree is a hierarchical data structure where each node has two children, left and right. It can have any type of values, and there is no restriction on the values of the nodes. A binary search tree (BST) is a special type of binary tree where the nodes follow a strict ordering rule: for any node, the left child’s value is smaller, and the right child’s value is larger. This property makes BSTs efficient for search, insertion, and deletion operations, all of which can be done in O(log n) time in the average case.

5. What is a hash table? How is it different from an array?

Explain that a hash table is a data structure that maps keys to values using a hash function, while an array is an indexed collection of elements.

Sample Answer:
A hash table is a data structure that stores key-value pairs and uses a hash function to map keys to specific indices in an array. This allows for O(1) average time complexity for lookups, insertions, and deletions. Arrays, on the other hand, are indexed collections of elements stored in contiguous memory. While arrays provide efficient random access to elements using indices, hash tables provide more efficient lookups by using keys. However, hash tables may experience collisions where multiple keys map to the same index, which requires handling strategies like chaining or open addressing.

6. Explain how a binary search algorithm works.

Describe the process of binary search and how it divides the search space in half with each iteration, providing O(log n) time complexity for sorted arrays or lists.

Sample Answer:
Binary search is a divide and conquer algorithm that works on sorted arrays or lists. It starts by comparing the target element with the middle element of the array. If the target is equal to the middle element, the search is successful. If the target is smaller, the search continues in the left half of the array, and if it’s larger, the search continues in the right half. This process repeats until the target is found or the search space is exhausted. The time complexity of binary search is O(log n) because with each comparison, it reduces the search space by half.

7. What is a heap, and how is it different from a binary search tree?

Explain that a heap is a special tree-based data structure that satisfies the heap property, whereas a binary search tree satisfies the BST property.

Sample Answer:
A heap is a complete binary tree where the heap property holds: for a max-heap, the value of each node is greater than or equal to the values of its children, and for a min-heap, the value of each node is less than or equal to the values of its children. Heaps are mainly used to implement priority queues. In contrast, a binary search tree (BST) follows the BST property: for any node, the left child’s value is smaller, and the right child’s value is larger. While a BST allows efficient search, insertion, and deletion, a heap ensures efficient access to the maximum or minimum element, but does not provide efficient search.

8. What is the time complexity of the quicksort algorithm?

Discuss the average case time complexity of quicksort and its worst-case behavior.

Sample Answer:
QuickSort is a divide and conquer algorithm that partitions the array around a pivot element. The average case time complexity of quicksort is O(n log n), as it divides the array into two subarrays and recursively sorts them. However, in the worst case, when the pivot is poorly chosen (e.g., the smallest or largest element), the time complexity becomes O(n²). To mitigate this, randomized quicksort or techniques like median-of-three pivot selection can be used to reduce the likelihood of worst-case performance.

9. Explain the difference between a depth-first search (DFS) and a breadth-first search (BFS).

Explain the traversal strategy of both DFS and BFS, highlighting their differences in terms of data structure used and order of node exploration.

Sample Answer:
Depth-first search (DFS) explores as far as possible along each branch before backtracking, using a stack (either explicitly or with recursion). Breadth-first search (BFS) explores all the neighbors of a node before moving on to their children, using a queue. DFS can be useful for exploring deep paths, such as in tree traversal or puzzle solving, while BFS is better for finding the shortest path in unweighted graphs, like in search algorithms or social network analysis.

10. What is dynamic programming? Give an example.

Define dynamic programming (DP) as a method for solving problems by breaking them down into smaller subproblems and solving each subproblem only once, storing the results for reuse.

Sample Answer:
Dynamic programming is a technique for solving problems by breaking them into overlapping subproblems, solving each subproblem once, and storing the results for reuse. A classic example is the Fibonacci sequence, where each number is the sum of the two preceding ones. Using DP, we can compute Fibonacci numbers efficiently by storing previously computed results instead of recalculating them. This reduces the time complexity from O(2ⁿ) in a naive recursive approach to O(n).

11. What is the time complexity of finding the minimum element in a binary search tree?

Mention that the time complexity is proportional to the height of the binary search tree (BST).

Sample Answer:
To find the minimum element in a binary search tree, we need to traverse the leftmost path of the tree, as the smallest element is always the leftmost leaf. The time complexity of this operation is O(h), where h is the height of the tree. In the best case, for a balanced tree, h is O(log n), but in the worst case (for an unbalanced tree), h can be O(n).

12. What is a trie, and how is it used?

Explain that a trie is a tree-like data structure used to store a dynamic set of strings, often used for tasks like autocomplete and spell checking.

Sample Answer:
A trie is a tree-like data structure used to store strings, where each node represents a character. The path from the root to a node represents a prefix of a string. Tries are used in autocomplete systems to quickly search for words with common prefixes. They provide efficient O(m) time complexity for searching, where m is the length of the string being searched, making them faster than hash tables for string-based queries.

13. What is a balanced binary tree?

Discuss the balance property of binary trees, focusing on how AVL trees and Red-Black trees maintain balance for efficient operations.

Sample Answer:
A balanced binary tree is a binary tree where the height difference between the left and right subtrees of any node is at most one. AVL trees and Red-Black trees are examples of self-balancing binary search trees, where operations like insertions and deletions maintain the tree’s balance. This ensures that the height of the tree is O(log n), allowing efficient operations like search, insert, and delete to run in O(log n) time.

14. How would you implement an algorithm to detect a cycle in a directed graph?

Describe a depth-first search (DFS)-based approach for detecting cycles, focusing on the concepts of visited nodes, recursion stack, and back edges.

Sample Answer:
To detect a cycle in a directed graph, I would perform a DFS traversal while keeping track of nodes in the recursion stack. If we encounter a node that is already in the recursion stack, we have found a cycle. I would use a visited array to track which nodes have been visited, and a recursion stack to track the nodes in the current DFS path. If a node is revisited within the current path, a cycle is detected. The time complexity of this approach is O(V + E), where V is the number of vertices and E is the number of edges.

15. Explain the difference between a greedy algorithm and dynamic programming.

Clarify that greedy algorithms make local optimal choices at each step, while dynamic programming solves problems by breaking them down into subproblems and solving them optimally.

Sample Answer:
A greedy algorithm makes the locally optimal choice at each step, with the hope of finding a global optimum. It doesn’t always produce the optimal solution but works for problems like activity selection or Huffman encoding. On the other hand, dynamic programming solves problems by breaking them down into smaller overlapping subproblems and storing the results of those subproblems to avoid redundant calculations. DP guarantees an optimal solution, whereas a greedy algorithm does not always guarantee an optimal result but is often more efficient in terms of time complexity.

Conclusion

By practicing these top 15 Algorithms and Data Structures interview questions, you’ll be well-prepared to tackle Microsoft’s technical interviews in 2026. The key is to not only focus on finding the right solution but also explaining your approach clearly and effectively. As with all coding interviews, practicing problem-solving and communicating your thought process are essential to success.

Good luck with your Microsoft interview preparation, and keep practicing to enhance your skills!