Coding Interview Resources

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List of most asked Programming Interview Questions. Are you preparing for a coding interview? This tweet is for you. It contains a list of the most asked interview questions from each topic. Arrays - How is an array sorted using quicksort? - How do you reverse an array? - How do you remove duplicates from an array? - How do you find the 2nd largest number in an unsorted integer array? Linked Lists - How do you find the length of a linked list? - How do you reverse a linked list? - How do you find the third node from the end? - How are duplicate nodes removed in an unsorted linked list? Strings - How do you check if a string contains only digits? - How can a given string be reversed? - How do you find the first non-repeated character? - How do you find duplicate characters in strings? Binary Trees - How are all leaves of a binary tree printed? - How do you check if a tree is a binary search tree? - How is a binary search tree implemented? - Find the lowest common ancestor in a binary tree? Graph - How to detect a cycle in a directed graph? - How to detect a cycle in an undirected graph? - Find the total number of strongly connected components? - Find whether a path exists between two nodes of a graph? - Find the minimum number of swaps required to sort an array. Dynamic Programming 1. Find the longest common subsequence? 2. Find the longest common substring? 3. Coin change problem? 4. Box stacking problem? 5. Count the number of ways to cover a distance?

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DSA question by understanding patterns If the input array is sorted then - Binary search - Two pointers If asked for all permutations/subsets then - Backtracking If given a tree then - DFS - BFS If given a graph then - DFS - BFS If given a linked list then - Two pointers If recursion is banned then - Stack If must solve in-place then - Swap corresponding values - Store one or more different values in the same pointer If asked for maximum/minimum subarray/ subset/options then - Dynamic programming If asked for top/least K items then - Heap - QuickSelect If asked for common strings then - Map - Trie Else - Map/Set for O(1) time & O(n) space - Sort input for O(nlogn) time and O(1) space

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C++ Programming Roadmap | |-- Fundamentals | |-- Basics of Programming | | |-- Introduction to C++ | | |-- Setting Up Development Environment (IDE: Code::Blocks, Visual Studio, etc.) | | |-- Compiling and Running C++ Programs | | | |-- Syntax and Structure | | |-- Basic Syntax | | |-- Variables and Data Types | | |-- Operators (Arithmetic, Relational, Logical, Bitwise) | |-- Control Structures | |-- Conditional Statements | | |-- If-Else Statements | | |-- Switch Case | | | |-- Loops | | |-- For Loop | | |-- While Loop | | |-- Do-While Loop | | | |-- Jump Statements | | |-- Break, Continue | | |-- Goto Statement | |-- Functions and Scope | |-- Defining Functions | | |-- Function Syntax | | |-- Parameters and Arguments (Pass by Value, Pass by Reference) | | |-- Return Statement | | | |-- Function Overloading | | |-- Overloading Functions with Different Parameters | | | |-- Scope and Lifetime | | |-- Local and Global Scope | | |-- Static Variables | |-- Object-Oriented Programming (OOP) | |-- Basics of OOP | | |-- Classes and Objects | | |-- Member Functions and Data Members | | | |-- Constructors and Destructors | | |-- Constructor Types (Default, Parameterized, Copy) | | |-- Destructor Basics | | | |-- Inheritance | | |-- Single and Multiple Inheritance | | |-- Protected Access Specifier | | |-- Virtual Base Class | | | |-- Polymorphism | | |-- Function Overriding | | |-- Virtual Functions and Pure Virtual Functions | | |-- Abstract Classes | | | |-- Encapsulation and Abstraction | | |-- Access Specifiers (Public, Private, Protected) | | |-- Getters and Setters | | | |-- Operator Overloading | | |-- Overloading Operators (Arithmetic, Relational, etc.) | | |-- Friend Functions | |-- Advanced C++ | |-- Pointers and Dynamic Memory | | |-- Pointer Basics | | |-- Dynamic Memory Allocation (new, delete) | | |-- Pointer Arithmetic | | | |-- References | | |-- Reference Variables | | |-- Passing by Reference | | | |-- Templates | | |-- Function Templates | | |-- Class Templates | | | |-- Exception Handling | | |-- Try-Catch Blocks | | |-- Throwing Exceptions | | |-- Standard Exceptions | |-- Data Structures | |-- Arrays and Strings | | |-- One-Dimensional and Multi-Dimensional Arrays | | |-- String Handling | | | |-- Linked Lists | | |-- Singly and Doubly Linked Lists | | | |-- Stacks and Queues | | |-- Stack Operations (Push, Pop, Peek) | | |-- Queue Operations (Enqueue, Dequeue) | | | |-- Trees and Graphs | | |-- Binary Trees, Binary Search Trees | | |-- Graph Representation and Traversal (DFS, BFS) | |-- Standard Template Library (STL) | |-- Containers | | |-- Vectors, Lists, Deques | | |-- Stacks, Queues, Priority Queues | | |-- Sets, Maps, Unordered Maps | | | |-- Iterators | | |-- Input and Output Iterators | | |-- Forward, Bidirectional, and Random Access Iterators | | | |-- Algorithms | | |-- Sorting, Searching, and Manipulation | | |-- Numeric Algorithms | |-- File Handling | |-- Streams and File I/O | | |-- ifstream, ofstream, fstream | | |-- Reading and Writing Files | | |-- Binary File Handling | |-- Testing and Debugging | |-- Debugging Tools | | |-- gdb (GNU Debugger) | | |-- Valgrind for Memory Leak Detection | | | |-- Unit Testing | | |-- Google Test (gtest) | | |-- Writing and Running Tests | |-- Deployment and DevOps | |-- Version Control with Git | | |-- Integrating C++ Projects with GitHub | |-- Continuous Integration/Continuous Deployment (CI/CD) | | |-- Using Jenkins or GitHub | | |   |--Free courses | | |--imp.i115008.net/kjoq9V |   |   |--imp.i115008.net/5bmnKL |   |   |--Microsoft Documentation |   |   |--Udemy Course Join @free4unow_backup for more free resources ENJOY LEARNING 👍👍

https://topmate.io/analyst/864764 If you're a job seeker, these well structured document resources will help you to know and learn all the real time SQL Interview questions with their exact answer. folks who are having 0-4+ years of experience have cracked the interview using this guide! Please use the above link to avail them!👆 NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your job search journey... All the best!👍✌️

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When you're studying DSA, you probably think, "This won't be directly used in the actual work of a company, so why am I even doing this?" And in life, where will this even come in handy? Well, it won't be useful directly, but the hard work you're putting in—sitting day and night solving questions—that habit of working hard will pay off. It's not really about DSA, but about the effort you're willing to give that will decide which company you land your internship or placement in ❤️

Clear all DSA rounds, By mastering these 20 DSA patterns 1. Fast and Slow Pointer - Cycle detection method - O(1) space efficiency - Linked list problems 2. Merge Intervals - Sort and merge - O(n log n) complexity - Overlapping interval handling 3. Sliding Window - Fixed/variable window - O(n) time optimization - Subarray/substring problems 4. Islands (Matrix Traversal) - DFS/BFS traversal - Connected component detection - 2D grid problems 5. Two Pointers - Dual pointer strategy - Linear time complexity - Array/list problems 6. Cyclic Sort - Sorting in cycles - O(n) time complexity - Constant space usage 7. In-place Reversal of Linked List - Reverse without extra space - O(n) time efficiency - Pointer manipulation technique 8. Breadth First Search - Level-by-level traversal - Uses queue structure - Shortest path problems 9. Depth First Search - Recursive/backtracking approach - Uses stack (or recursion) - Tree/graph traversal 10. Two Heaps - Max and min heaps - Median tracking efficiently - O(log n) insertions 11. Subsets - Generate all subsets - Recursive or iterative - Backtracking or bitmasking 12. Modified Binary Search - Search in variations - O(log n) time - Rotated/specialized arrays 13. Bitwise XOR - Toggle bits operation - O(1) space complexity - Efficient for pairing 14. Top 'K' elements - Use heap/quickselect - O(n log k) time - Efficient selection problem 15. K-way Merge - Merge sorted lists - Min-heap based approach - O(n log k) complexity 16. 0/1 Knapsack (Dynamic Programming) - Choose or skip items - O(n * W) complexity - Maximize value selection 17. Unbounded Knapsack (Dynamic Programming) - Unlimited item choices - O(n * W) complexity - Multiple item selection 18. Topological Sort (Graphs) - Directed acyclic graph - Order dependency resolution - Uses DFS or BFS 19. Monotonic Stack - Maintain increasing/decreasing stack - Optimized for range queries - O(n) time complexity 20. Backtracking - Recursive decision-making - Explore all possibilities - Pruning with constraints Best DSA Resources: 👇 https://topmate.io/coding/886874 All the best 👍👍

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https://topmate.io/coding/886874 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! Please use the above link to avail them!👆 NOTE: -Most people hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it. Hope this helps in your job search journey... All the best!👍✌️

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In class, there are some students who are really good at coding from the start, and seeing them can make us feel quite demotivated, especially since they often appear overconfident. But it's not important how much someone already knows. If you start and practice consistently, it's not that tough to match their level or even surpass them. And often, these overconfident people don’t perform as well as you can because you have the desire to learn, while they think they already know everything. So, my friend, don’t get demotivated—just give it time!

How long are coding interviews? The phone screen portion of the coding interview typically lasts up to one hour. The second, more technical part of the interview can take multiple hours. Where can I practice coding? There are many ways to practice coding and prepare for your coding interview. LeetCode provides practice opportunities in more than 14 languages and more than 1,500 sample problems. Applicants can also practice their coding skills and interview prep with HackerRank. How do I know if my coding interview went well? There are a variety of indicators that your coding interview went well. These may include going over the allotted time, being introduced to additional team members, and receiving a quick response to your thank you email.

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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 You can check these resources for Coding interview Preparation Credits: https://t.me/free4unow_backup All the best 👍👍

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