DSA INTERVIEW QUESTIONS AND ANSWERS

1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.

2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.

3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.

4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.

5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).

6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.

7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.

8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.

Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).

Time complexity: best case O(n2); worst O(n2)

Space complexity: worst O(1)

9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them

10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.

11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect

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📂 Programming Basics
 ∟📂 Master C++ Syntax, Variables & Data Types
  ∟📂 Learn Control Flow, Loops & Functions
   ∟📂 Practice with Simple Programs

📂 Object-Oriented Programming (OOP)
 ∟📂 Understand Classes, Objects & Inheritance
  ∟📂 Dive into Encapsulation, Polymorphism & Abstraction
   ∟📂 Explore Templates & the Standard Template Library (STL)

📂 Memory Management & Pointers
 ∟📂 Grasp Pointers, References & Dynamic Memory Allocation
  ∟📂 Master Manual Memory Management
   ∟📂 Learn Smart Pointers & RAII Principles

📂 Data Structures & Algorithms
 ∟📂 Study Arrays, Vectors, Lists, Maps & Sets
  ∟📂 Understand Sorting, Searching & Recursion
   ∟📂 Solve Coding Challenges to Reinforce Concepts

📂 Tools & Build Systems
 ∟📂 Get Comfortable with IDEs (e.g., Visual Studio, CLion)
  ∟📂 Learn CMake & Other Build Tools
   ∟📂 Master Git & Version Control Systems

📂 Advanced C++ Concepts
 ∟📂 Explore Lambda Functions & Modern C++ Features
  ∟📂 Understand Multithreading & Concurrency
   ∟📂 Dive into Performance Optimization & Best Practices

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 ∟📂 Learn Debugging Techniques & Tools
  ∟📂 Master Unit Testing with Frameworks (e.g., Google Test)
   ∟📂 Analyze and Optimize Code Performance

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Dear software engineers,

It stings when you see your college friends or ex-teammates posting about new job offers, hikes, or “finally made it to FAANG” while you’re still hustling for your shot.

Every “I’m thrilled to announce…” on LinkedIn can feel like salt in the wound.

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But please understand that everyone’s journey in tech runs on a different timeline.

Some folks have been grinding DSA or building side projects for years.
Some get lucky with a referral or the right timing.
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Here are 40 most asked DSA questions to ace your next interview -

𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 (𝗗𝗣):
1. How do you find the nth Fibonacci number using dynamic programming?
2. Write a dynamic programming solution for the 0/1 knapsack problem.
3. Memoization to optimize recursive solutions in dynamic programming?
4. Implement a dynamic programming algorithm to find the longest common subsequence of two strings.
5. The coin change problem.
6. Tabulation approach in dynamic programming.

𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴:
7. Backtracking algorithm to solve the N-Queens problem.
8. Generate all permutations of a given set using backtracking?
9. Implement backtracking to solve the Sudoku puzzle.
10. Subset sum problem.
11. Graph coloring problem using backtracking.
12. Write a backtracking algorithm to find the Hamiltonian cycle in a graph.

𝗛𝗮𝘀𝗵𝗶𝗻𝗴:
13. Implement a hash table using separate chaining.
14. First non-repeating character in a string using hashing.
15. Collision resolution techniques in hashing.
16. Write a function to solve the two-sum problem using hashing.
17. How can you implement a hash set data structure?
18. Count the frequency of elements in an array using hashing.

𝗛𝗲𝗮𝗽:
19. Implement a priority queue using a min-heap.
20. How do you merge K sorted arrays using a min-heap?
21. Write a function to perform heap sort algorithm.
22. Find the kth largest element in an array using a min-heap.
23. Implement a priority queue using a min-heap.
24. How do you build a max heap from an array?

𝗧𝗿𝗶𝗲𝘀:
25. Implement a trie data structure.
26. Write a function to search for a word in a trie.
27. How can you implement autocomplete feature using a trie?
28. Deleting a word from a trie.
30. Write a function to find all words matching a pattern in a trie.

𝗚𝗿𝗲𝗲𝗱𝘆 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀:
31. Solve the activity selection problem using a greedy algorithm.
32. Implement Huffman coding using a greedy algorithm.
33. Write a function to find the minimum spanning tree using Prim's algorithm.
34. Coin change problem.
35. Dijkstra's algorithm using a greedy approach.
36. Implement the job sequencing problem using a greedy algorithm.


37. Stack Vs queue.
38. breadth-first search (BFS) and depth-first search (DFS) traversal
39. Concept of big O notation.
40. What is an AVL tree? Explain its properties and how it maintains balance during insertion and deletion operations.

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These are top 5 data structures and algorithms projects, allowing you to dive deep into the world of DSA 💪🏻

•Project 1: Snakes Game (Arrays)

The Snakes Game project is a classic implementation of the popular game
Snake.

This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups.

•Project 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps)

The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts

•Project 3: Sudoku Solver (Backtracking)

The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems.

•Project 4: File Zipper (Greedy Huffman
Encoder)

The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between
compression ratio and execution time.

•Project 5: Map Navigator (Dijkstra’s
Algorithm)

The Map Navigator project aims to develop a navigation system using Dijkstra’s algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic.

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✅Meta interview questions : Most asked in last 30 days

1. 1249. Minimum Remove to Make Valid Parentheses

2. 408. Valid Word Abbreviation

3. 215. Kth Largest Element in an Array

4. 314. Binary Tree Vertical Order Traversal

5. 88. Merge Sorted Array

6. 339. Nested List Weight Sum

7. 680. Valid Palindrome II

8. 973. K Closest Points to Origin

9. 1650. Lowest Common Ancestor of a Binary Tree III

10. 1. Two Sum

11. 791. Custom Sort String

12. 56. Merge Intervals

13. 528. Random Pick with Weight

14. 1570. Dot Product of Two Sparse Vectors

15. 50. Pow(x, n)

16. 65. Valid Number

17. 227. Basic Calculator II

18. 560. Subarray Sum Equals K

19. 71. Simplify Path

20. 200. Number of Islands

21. 236. Lowest Common Ancestor of a Binary Tree

22. 347. Top K Frequent Elements

23. 498. Diagonal Traverse

24. 543. Diameter of Binary Tree

25. 1768. Merge Strings Alternately

26. 2. Add Two Numbers

27. 4. Median of Two Sorted Arrays

28. 7. Reverse Integer

29. 31. Next Permutation

30. 34. Find First and Last Position of Element in Sorted Array

31. 84. Largest Rectangle in Histogram

32. 146. LRU Cache

33. 162. Find Peak Element

34. 199. Binary Tree Right Side View

35. 938. Range Sum of BST

36. 17. Letter Combinations of a Phone Number
37. 125. Valid Palindrome

38. 153. Find Minimum in Rotated Sorted Array

39. 283. Move Zeroes

40. 523. Continuous Subarray Sum

41. 658. Find K Closest Elements

42. 670. Maximum Swap

43. 827. Making A Large Island

44. 987. Vertical Order Traversal of a Binary Tree

45. 1757. Recyclable and Low Fat Products

46. 1762. Buildings With an Ocean View

47. 2667. Create Hello World Function

48. 5. Longest Palindromic Substring

49. 15. 3Sum

50. 19. Remove Nth Node From End of List

51. 70. Climbing Stairs

52. 80. Remove Duplicates from Sorted Array II

53. 113. Path Sum II

54. 121. Best Time to Buy and Sell Stock

55. 127. Word Ladder

56. 128. Longest Consecutive Sequence

57. 133. Clone Graph

58. 138. Copy List with Random Pointer

59. 140. Word Break II

60. 142. Linked List Cycle II

61. 145. Binary Tree Postorder Traversal

62. 173. Binary Search Tree Iterator

63. 206. Reverse Linked List

64. 207. Course Schedule

65. 394. Decode String

66. 415. Add Strings

67. 437. Path Sum III

68. 468. Validate IP Address

70. 691. Stickers to Spell Word

71. 725. Split Linked List in Parts

72. 766. Toeplitz Matrix

73. 708. Insert into a Sorted Circular Linked List

74. 1091. Shortest Path in Binary Matrix

75. 1514. Path with Maximum Probability

76. 1609. Even Odd Tree

77. 1868. Product of Two Run-Length Encoded Arrays

78. 2022. Convert 1D Array Into 2D Array

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Top Libraries & Frameworks by Language 📚💻

❯ Python
 • Pandas ➟ Data Analysis
 • NumPy ➟ Math & Arrays
 • Scikit-learn ➟ Machine Learning
 • TensorFlow / PyTorch ➟ Deep Learning
 • Flask / Django ➟ Web Development
 • OpenCV ➟ Image Processing

❯ JavaScript / TypeScript
 • React ➟ UI Development
 • Vue ➟ Lightweight SPAs
 • Angular ➟ Enterprise Apps
 • Next.js ➟ Full-Stack Web
 • Express ➟ Backend APIs
 • Three.js ➟ 3D Web Graphics

❯ Java
 • Spring Boot ➟ Microservices
 • Hibernate ➟ ORM
 • Apache Maven ➟ Build Automation
 • Apache Kafka ➟ Real-Time Data

❯ C++
 • Boost ➟ Utility Libraries
 • Qt ➟ GUI Applications
 • Unreal Engine ➟ Game Development

❯ C#
 • .NET / ASP.NET ➟ Web Apps
 • Unity ➟ Game Development
 • Entity Framework ➟ ORM

❯ R
 • ggplot2 ➟ Data Visualization
 • dplyr ➟ Data Manipulation
 • caret ➟ Machine Learning
 • Shiny ➟ Interactive Dashboards

❯ PHP
 • Laravel ➟ Full-Stack Web
 • Symfony ➟ Web Framework
 • PHPUnit ➟ Testing

❯ Go (Golang)
 • Gin ➟ Web Framework
 • Gorilla ➟ Web Toolkit
 • GORM ➟ ORM for Go

❯ Rust
 • Actix ➟ Web Framework
 • Rocket ➟ Web Development
 • Tokio ➟ Async Runtime

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💻 Popular Coding Languages & Their Uses 🚀

There are many programming languages, each serving different purposes. Here are some key ones you should know:

🔹 1. Python – Beginner-friendly, versatile, and widely used in data science, AI, web development, and automation.

🔹 2. JavaScript – Essential for frontend and backend web development, powering interactive websites and applications.

🔹 3. Java – Used for enterprise applications, Android development, and large-scale systems due to its stability.

🔹 4. C++ – High-performance language ideal for game development, operating systems, and embedded systems.

🔹 5. C# – Commonly used in game development (Unity), Windows applications, and enterprise software.

🔹 6. Swift – The go-to language for iOS and macOS development, known for its efficiency.

🔹 7. Go (Golang) – Designed for high-performance applications, cloud computing, and network programming.

🔹 8. Rust – Focuses on memory safety and performance, making it great for system-level programming.

🔹 9. SQL – Essential for database management, allowing efficient data retrieval and manipulation.

🔹 10. Kotlin – Popular for Android app development, offering modern features compared to Java.

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