Unpopular opinion: FAANG job interviews are harder than the actual job.

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API design involves making decisions about how your API will interact with consumers.

The major API design choices are:

REST (Representational State Transfer): It uses HTTP methods (GET, POST, PUT, DELETE) for CRUD operations on resources. It's stateless and commonly used for web-based APIs

GraphQL: Query language for APIs, it allows clients to request specific data

RPC (Remote Procedure Call): High-performance, language-agnostic framework, often used in microservices architectures

A doubly linked list has two pointers ‘left’ and ‘right’ which enable it to traverse in either direction. Compared to singly liked list which has only a ‘next’ pointer, doubly linked list requires extra space to store this extra pointer. Every insertion and deletion requires manipulation of two pointers, hence it takes a bit longer time. Implementing doubly linked list involves setting both left and right pointers to correct nodes and takes more time than singly linked list.

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If you're a job seeker, these well structured document DSA resources will help you to know and learn all the real time DSA & OOPS Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide!

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Programming is no longer about how well you google search.

Programming is now about how well you can write prompts for an ai system to generate code for you

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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How to guess the solution for DSA problems?

Yes, it is possible.

You can predict the solution for a problem by analyzing the constraints.

Curious if you need a greedy approach or a backtracking solution? Trying to decide between an O(n^3) or an O(n log n) approach? Just scroll down the LeetCode question and look at the constraints of the main element.

Wondering if you should use dynamic programming or plain recursion? Should your solution be O(n^2) or O(n)? Simply examine the constraints of the main variable.

Here's a quick guide based on the value of (n):

- If n <= 12 Time complexity can be O(n!).
- If n <= 25 Time complexity can be O(2^n).
- If n <= 100 Time complexity can be O(n^4).
- If n <= 500 Time complexity can be O(n^3).
- If n <= 10 ^ 4 Time complexity can be O(n^2).
- If n <= 10 ^ 6 Time complexity can be O(n log n).
- If n <= 10 ^ 8 Time complexity can be O(n).
- If n > 10 ^ 8 Time complexity can be O(log n) or 0(1).
- If n <= 10 ^ 9 Time complexity can be O(sqrt{n}).
- If n > 10 ^ 9 Time complexity can be O(log n) or 0(1).

Understanding these constraints can help you choose the right algorithm and improve your problem-solving efficiency.

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Top 9 Http Methods-

GET 🧐 - Retrieve data from a resource.
HEAD 🎧 - Retrieve the headers of a resource.
POST 📮 - Submit data to a resource.
PUT 📥 - Update an existing resource or create a new resource.
DELETE 🗑️ - Remove a resource.
CONNECT 🔗 - Establish a network connection for a resource.
OPTIONS ⚙️ - Describe communication options for the target resource.
TRACE 🕵️‍♂️ - Retrieve a diagnostic trace of the request.
PATCH 🩹 - Apply a partial update to a resource.

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Problem: Given an array a of n integers, find all such elements a[i], a[j], a[k], and a[l], such that a[i] + a[j] + a[k] + a[l] = target? Output all unique quadruples.

Solution: of course one way would be to just use 4 nested loops to iterate over all possible quadruples, but this is quite slow O(n^4). Another way is to iterate over all triples, put the sums into a set and then in another pass over elements a[i] check if we have any triple with sum (T - a[i]). This would give us O(n^3), and we need to keep track of which elements gave us the required sums. Another step is to iterate over all pairs and put results into a map from integer to indexes of elements, which produce this sum. Then in another pass over this map we can see if we can get a sum of T using two different values from the map (and they shouldn't be using the same element twice). This approach has time complexity O(n^2).

Solve these 16 problem solving patterns to learn enough DSA

1. Sliding window pattern.
2. Two pointer pattern.
3. Fast & slow pointers pattern.
4. Merger interval pattern.
5. Cyclic sort pattern.
6. In-place reversal of linked list pattern.
7. Tree breadth first search pattern.
8. Depth first search DFS pattern.
9. Two heap pattern.
10. Subsets pattern.
11. Modified binary search pattern.
12. Bitwise xor pattern.
13. Top 'K' elements pattern.
14. K-way merge pattern.
15. 0/1 knapsack DP pattern.
16. Topological sort Graph pattern.

𝗦𝘁𝗲𝗽𝘀 𝗧𝗼 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗙𝗼𝗿 𝗮 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄

👉 𝗞𝗻𝗼𝘄 𝘁𝗵𝗲 𝗝𝗼𝗯: Review the job description.
👉 𝗕𝗮𝘀𝗶𝗰𝘀: Revise fundamental concepts.
👉 𝗖𝗼𝗱𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Solve coding problems.
👉 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Be ready to discuss past work.
👉 𝗠𝗼𝗰𝗸 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀: Practice with friends or online.
👉 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻: Review basics if needed.
👉 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: Prepare some for the interviewer.
👉 𝗥𝗲𝘀𝘁: Sleep well and stay calm.

Remember, practice and confidence are the key! Good luck with your technical interview! 🌟👍

You can check these resources for Coding interview Preparation

All the best 👍👍

Beginner’s Roadmap to Learn Data Structures & Algorithms

1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.

2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.

3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.

4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.

5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.

6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.

7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.

8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.

9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.

10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.

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Is DSA important for interviews?

Yes, DSA (Data Structures and Algorithms) is very important for interviews, especially for software engineering roles.

I often get asked, What do I need to start learning DSA?

Here's the roadmap for getting started with Data Structures and Algorithms (DSA):

𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
1. Introduction to DSA
- Understand what DSA is and why it's important.
- Overview of complexity analysis (Big O notation).

2. Complexity Analysis
- Time Complexity
- Space Complexity

3. Basic Data Structures
- Arrays
- Linked Lists
- Stacks
- Queues

4. Basic Algorithms
- Sorting (Bubble Sort, Selection Sort, Insertion Sort)
- Searching (Linear Search, Binary Search)

5. OOP (Object-Oriented Programming)

𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
1. Two Pointers Technique
- Introduction and basic usage
- Problems: Pair Sum, Triplets, Sorted Array Intersection etc..

2. Sliding Window Technique
- Introduction and basic usage
- Problems: Maximum Sum Subarray, Longest Substring with K Distinct Characters, Minimum Window Substring etc..

3. Line Sweep Algorithms
- Introduction and basic usage
- Problems: Meeting Rooms II, Skyline Problem

4. Recursion

5. Backtracking

6. Sorting Algorithms
- Merge Sort
- Quick Sort

7. Data Structures
- Hash Tables
- Trees (Binary Trees, Binary Search Trees)
- Heaps

𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
1. Graph Algorithms
- Graph Representation (Adjacency List, Adjacency Matrix)
- BFS (Breadth-First Search)
- DFS (Depth-First Search)
- Shortest Path Algorithms (Dijkstra's, Bellman-Ford)
- Minimum Spanning Tree (Kruskal's, Prim's)

2. Dynamic Programming
- Basic Problems (Fibonacci, Knapsack etc..)
- Advanced Problems (Longest Increasing Subsea mice, Matrix Chain Subsequence, Multiplication etc..)

3. Advanced Trees
- AVL Trees
- Red-Black Trees
- Segment Trees
- Trie

𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻
1. Competitive Programming Platforms: LeetCode, Codeforces, HackerRank, CodeChef Solve problems daily

2. Mock Interviews
- Participate in mock interviews to simulate real interview scenarios.
- DSA interviews assess your ability to break down complex problems into smaller steps.

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When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:

➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.

➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.

➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?

➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.

➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.

➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got.

➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- Talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?

➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. What new skills did you gain, and what would you do differently next time?

➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.

By preparing your project details thoroughly and understanding what the interviewer is looking for, you can talk about your experience in a way that really showcases your skills and increases your chances of getting the job.

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🏟 Here is a complete roadmap to learn Data Structures and Algorithms (DSA) 🏟


1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.

2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.

3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.

4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.

5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.

6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.

7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.

8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.

9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.

10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.

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𝗦𝘁𝗲𝗽𝘀 𝗧𝗼 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗙𝗼𝗿 𝗮 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄

👉 𝗞𝗻𝗼𝘄 𝘁𝗵𝗲 𝗝𝗼𝗯: Review the job description.
👉 𝗕𝗮𝘀𝗶𝗰𝘀: Revise fundamental concepts.
👉 𝗖𝗼𝗱𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Solve coding problems.
👉 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Be ready to discuss past work.
👉 𝗠𝗼𝗰𝗸 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀: Practice with friends or online.
👉 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻: Review basics if needed.
👉 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: Prepare some for the interviewer.
👉 𝗥𝗲𝘀𝘁: Sleep well and stay calm.

Remember, practice and confidence are the key! Good luck with your technical interview! 🌟👍

You can check these resources for Coding interview Preparation

All the best 👍👍

⭕ WIPRO INTERVIEW EXPERIENCE ⭕

1) Intro.
2) Willing to Relocate.
3) Service Agreement.
4) about Internship (if you have done)
   (details like what did you do there or
   learnt there, how many other people
   you worked with).
5) About Project ( details like name,
     what made you do that project, what
     Tech Stacks used, Difficulties faced,
     whats the use of that project ).
6) In which Programming language you
    are proficient (i said python ).
7) Why python and why not any other
    languages.
8) Any one simple Theoritical question
    from that language you mentioned
   (asked limitations of python).

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Best suited IDE's for programming languages:

1. JavaScript => VSCode
2. Python => PyCharm
3. C# => Visual Studio
4. Java => IntelliJ IDEA
5. Ruby => Ruby Mine
6. C & C++ => CLion