Data Structures & Algorithms Interview Questions (2025)
Welcome to JaganInfo’s comprehensive Data Structures and Algorithms interview preparation guide (2025)! This set covers top 30 interview questions and answers—Basic, Intermediate, Advanced—with tips and real-world scenarios for both freshers and experienced professionals preparing for software jobs.
Basic Level Interview Questions
- What is a data structure?
A data structure is a way to organize, manage, and store data for efficient access and modification.
- Explain the difference between an array and a linked list.
Arrays use contiguous memory and offer fast random access, but fixed size. Linked lists use nodes connected by pointers, allow dynamic size, but slower access.
- What is a stack and how does it work?
Stack is LIFO (Last In First Out). Push to add, pop to remove. Used in function calls, undo/redo.
- What is a queue?
Queue is FIFO (First In First Out). Enqueue to add, dequeue to remove. Used in printers, CPU scheduling.
- What is a linked list?
A series of nodes where each node contains data and a pointer to the next node. Types: singly, doubly, circular.
- What is the difference between linear and nonlinear data structures?
Linear (array, stack, queue) elements in sequence; Nonlinear (tree, graph) organized hierarchically or in networks.
- What operations can be performed on arrays?
Insert, delete, update, traverse, search, sort.
- What is a dynamic array?
Array that resizes automatically when capacity is exceeded. Example: ArrayList in Java, Vector in C++.
- What are the advantages of linked lists over arrays?
Dynamic sizing, easy insertion/deletion, but higher memory and no random access.
- What is a circular queue?
Queue where the last position points to the first position, forming a circle. Prevents wasted space with shifting.
Intermediate Level Interview Questions
- What is a binary tree?
Hierarchical structure where each node has up to two children (left/right). Used for efficient searching/sorting.
- What is a binary search tree (BST)?
Special binary tree where left child < node < right child. Allows fast searching, insertion, deletion.
- Explain different tree traversals.
Inorder (LNR), Preorder (NLR), Postorder (LRN), Level Order (BFS).
- What is recursion? Give example.
Function calling itself. Example: Calculating factorial recursively.
- What is hashing? How are collisions handled?
Hashing maps keys to indexes. Collisions (same index) handled by chaining (linked lists) or open addressing.
- Explain depth-first search (DFS) and breadth-first search (BFS).
DFS goes deep along a branch, BFS explores all neighbors at each depth first. Used in graphs and trees.
- What is a heap data structure? Types?
Complete binary tree used as max-heap (parent ≥ children) or min-heap (parent ≤ children). Used in priority queues.
- Explain dynamic programming.
Technique to solve problems by breaking into smaller overlapping subproblems, storing solutions (memoization).
- What is a graph? Types of graphs?
Vertices and edges. Types: directed/undirected, weighted/unweighted, cyclic/acyclic.
- What is a hash table and where is it used?
Structure based on hashing for efficient storage and retrieval. Used in dictionaries, caches, sets, etc.
Advanced Level Interview Questions
- What is a trie and its application?
Tree-like structure for storing strings efficiently. Used in autocomplete, spell-check, IP routing.
- Explain time and space complexity of Merge Sort.
Time complexity: O(n log n) best/average/worst. Space complexity: O(n) for extra arrays used in merging.
- Describe graph traversal algorithms (DFS & BFS).
DFS: uses stack/recursion; BFS: uses queue. BFS finds shortest path in unweighted graphs.
- What is topological sorting in a graph?
Ordering vertices in a Directed Acyclic Graph (DAG) so for every directed edge u→v, u comes before v.
- What is a balanced binary tree? Examples?
Tree where height difference of subtrees ≤ 1. Example: AVL tree, Red-Black tree.
- Explain Dijkstra’s algorithm.
Finds shortest path from source to all vertices in a weighted graph with non-negative edges.
- What is Floyd’s Cycle Finding Algorithm?
Detects cycles in linked lists using two pointers (slow/fast). Also called Tortoise and Hare algorithm.
- What is a segment tree? Applications?
Advanced tree for range queries like sum/min/max over subarrays. Efficient for dynamic intervals.
- Explain amortized analysis with an example.
Analyzes average time per operation over sequence, e.g., array resizing: most appends fast, occasional resizing costly, but average remains O(1).
- How will you implement an LRU (Least Recently Used) cache?
Use a hashmap and doubly-linked list for O(1) access and update. Remove least-recent node when full.
Tips for Cracking DS & Algo Interviews
- Practice LeetCode, HackerRank, GeeksforGeeks problems regularly.
- Review time and space complexities (Big O notation) for all standard algorithms.
- Draw diagrams to visualize data structures and their operations.
- Explain your logic clearly during interviews, even if you get stuck.
- Study implementation and uses of arrays, linked lists, stacks, queues, trees, graphs, hash tables, and heaps.
- Write code by hand—improves confidence and debugging in real interviews.
- Cover common patterns: sliding window, two pointers, divide and conquer, backtracking, and dynamic programming.
- Practice commonly asked system design and scenario-based questions for senior roles.
- Pair up with a friend for mock interviews and timed contests.
- Check interview experiences on Glassdoor and GeeksforGeeks for company-specific questions.
Bonus: Real-World Scenario Questions
- How would you design a URL shortening service like bit.ly?
Use a hash table for mapping short to long URLs, handle collisions, and maintain a database for persistence.
- How do you detect a cycle in a directed graph?
Use DFS and maintain a recursion stack or use Kahn’s algorithm (topological sort) to detect cycles.
- Design an efficient autocomplete system.
Use a trie data structure to store words and perform prefix search efficiently, with frequency tracking for ranking.
- Implement a text editor’s undo feature efficiently.
Use a stack to keep track of states; each state can be reversed using another stack (redo feature).
- How do you implement a browser’s forward and back button?
Use two stacks: one for history (back), another for future (forward). Move URLs between them as user navigates.
- Find the first non-repeated character in a string.
Use a hash map to store frequency counts, then iterate the string to find the first character with count 1.
- How to merge k sorted linked lists efficiently?
Use a min heap to track the first node of each list, repeatedly extract min and insert next node from same list.
- Find the k-th largest element in an array.
Use a min heap of size k or QuickSelect algorithm for average O(n) time.
- Implement a Sudoku solver.
Use backtracking (recursion) to try filling valid numbers—go back on conflicts.
- How to detect and remove a loop in a linked list?
Use Floyd’s algorithm to detect loop, then move one pointer to head and another at meeting point; move both one step until they meet again to find loop start, then remove loop.
For more job-oriented Q&A, check other sections on AWS, Java, Python, Testing, Data Science, and Cloud in JaganInfo!
All the best for your interviews in 2025!