Your biggest enemy 𝐅𝐄𝐀𝐑 𝐨𝐟 𝐑𝐞𝐣𝐞𝐜𝐭𝐢𝐨𝐧

People hesitate to apply for many opportunities just because of fear of rejection.

However, not applying means you are automatically rejecting yourself. They usually think I will start applying after 6-8 months with full preparation.

Do you really think it will work ??? Interview calls usually take months 😅

My suggestion would be to start applying after 10 days to 1 month of preparation . Try to give as many interviews as you can. In this way, you will learn 👇🏻

🌴 Frequently asked questions
🌴 Interview pattern
🌴 How to tweak your answers?

Give a try ,even in the worst scenario, you will get some interview experience. That experience will eventually help you in the future

All the best 👍👍

Top 7 Must-Prepare Topics for Coding Interviews (2025 Edition)

✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc.
✅ Linked Lists – Practice reversal, cycle detection, and merging lists
✅ Hashing & Maps – Use hash tables for fast lookups and frequency-based problems
✅ Recursion & Backtracking – Solve problems like permutations, subsets, and Sudoku
✅ Dynamic Programming – Understand memoization, tabulation, and classic patterns
✅ Trees & Graphs – Cover traversal (BFS/DFS), shortest paths, and tree operations
✅ Stacks & Queues – Solve problems involving monotonic stacks, parentheses, and sliding windows

These are the essentials to crack FAANG-level interviews or product-based companies.

Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

ENJOY LEARNING 👍👍

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Here are some common frontend interview questions along with brief answers:

1. What is the DOM (Document Object Model)?
- Answer: The DOM is a programming interface for web documents. It represents the structure of a web page and allows scripts to dynamically access and update the content, structure, and style of a webpage.

2. Explain the difference between null and undefined in JavaScript.
- Answer: null represents the intentional absence of any object value, while undefined represents a variable that has been declared but has not been assigned a value.

3. What are closures in JavaScript?
- Answer: Closures are functions that remember the scope in which they were created, even after that scope has exited. They have access to variables from their containing function's scope.

4. Describe the differences between CSS Grid and Flexbox.
- Answer: CSS Grid is a two-dimensional layout system, while Flexbox is one-dimensional. Grid is used for overall layout structure, while Flexbox is ideal for distributing space and aligning items within a container along a single axis.

5. What is responsive web design, and how do you achieve it?
- Answer: Responsive web design is an approach to design and coding that makes web pages render well on various devices and screen sizes. Achieve it through media queries, flexible grids, and fluid images.

6. Explain the "box model" in CSS.
- Answer: The box model describes how elements on a web page are rendered. It consists of content, padding, border, and margin, and these properties determine the element's total size.

7. How does the event delegation work in JavaScript?
- Answer: Event delegation is a technique where you attach a single event listener to a common ancestor of multiple elements instead of attaching listeners to each element individually. Events that bubble up from child elements can be handled by the ancestor.

8. What is the purpose of the localStorage and sessionStorage objects in JavaScript?
- Answer: Both localStorage and sessionStorage allow you to store key-value pairs in a web browser. The key difference is that data stored in localStorage persists even after the browser is closed, whereas data in sessionStorage is cleared when the session ends (e.g., when the browser is closed).

9. Explain the same-origin policy in the context of web security.
- Answer: The same-origin policy is a security measure that restricts web pages from making requests to a different domain (protocol, port, or host) than the one that served the web page. It helps prevent cross-site request forgery (CSRF) and other security vulnerabilities.

10. What are the benefits of using a CSS preprocessor like Sass or Less?
- Answer: CSS preprocessors provide benefits such as variables, nesting, functions, and mixins, which enhance code reusability, maintainability, and organization. They allow you to write cleaner and more efficient CSS.

Web Development Best Resources: https://topmate.io/coding/930165

ENJOY LEARNING 👍👍

🔴 How to MASTER a programming language using ChatGPT : 📌

1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?

2. What are some commonly asked interview questions about [lang]?

3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.

4. Give me some practice questions along with solutions for [concept] in [lang].

5. What are some common mistakes that people make in [lang]?

6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?

7. How can I optimize the performance of my code in [lang]?

8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?

9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?

10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?

11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?

12. How can I effectively collaborate with other developers in [lang] on a project?

13. What are some common data structures and algorithms that I should be familiar with in [lang]?

Join for more: https://t.me/AI_Best_Tools

Python Interview Questions – Part 1

1. What is Python?
Python is a high-level, interpreted programming language known for its readability and wide range of libraries.

2. Is Python statically typed or dynamically typed?
Dynamically typed. You don't need to declare data types explicitly.

3. What is the difference between a list and a tuple?

List is mutable, can be modified.

Tuple is immutable, cannot be changed after creation.


4. What is indentation in Python?
Indentation is used to define blocks of code. Python strictly relies on indentation instead of brackets {}.

5. What is the output of this code?

x = [1, 2, 3]
print(x * 2)

Answer: [1, 2, 3, 1, 2, 3]

6. Write a Python program to check if a number is even or odd.

num = int(input("Enter number: "))
if num % 2 == 0:
print("Even")
else:
print("Odd")

7. What is a Python dictionary?
A collection of key-value pairs. Example:

person = {"name": "Alice", "age": 25}

8. Write a function to return the square of a number.

def square(n):
return n * n


Coding Interviews: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

ENJOY LEARNING 👍👍

Technical Questions Wipro may ask on their interviews

1. Data Structures and Algorithms:
   - "Can you explain the difference between an array and a linked list? When would you use one over the other in a real-world application?"
   - "Write code to implement a binary search algorithm."

2. Programming Languages:
   - "What is the difference between Java and C++? Can you provide an example of a situation where you would prefer one language over the other?"
   - "Write a program in your preferred programming language to reverse a string."

3. Database and SQL:
   - "Explain the ACID properties in the context of database transactions."
   - "Write an SQL query to retrieve all records from a 'customers' table where the 'country' column is 'India'."

4. Networking:
   - "What is the difference between TCP and UDP? When would you choose one over the other for a specific application?"
   - "Explain the concept of DNS (Domain Name System) and how it works."

5. System Design:
   - "Design a simple online messaging system. What components would you include, and how would they interact?"
   - "How would you ensure the scalability and fault tolerance of a web service or application?"

Software Engineer: C++ C# Java, Python, JavaScript

Web Dev: HTML, CSS, JavaScript, NodeJS

Game Dev: Unity, Unreal, Java

App Dev: Flutter, Objective C, Java, Swift, Kotlin, React

Cyber Security: Python, Linux, Networking

AI & Data Science - Julia, Haskell

Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems

Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report

Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)

Best DSA RESOURCES: https://topmate.io/coding/886874

Credits: https://t.me/free4unow_backup

ENJOY LEARNING 👍👍

Theoretical Questions for Coding Interviews on Basic Data Structures

1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.

2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.

3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.

4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).

5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).

6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.

7. What is the difference between an array and a linked list?

Array: Fixed size, elements stored in contiguous memory.

Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.


8. What is the time complexity for accessing an element in an array vs. a linked list?

Array: O(1) for direct access by index.

Linked List: O(n) for access, as you must traverse the list from the start to find an element.


9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?

Array:

Insertion/Deletion at the end: O(1).

Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.


Linked List:

Insertion/Deletion at the beginning: O(1).

Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.



10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).

Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems

Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report

Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)

Best DSA RESOURCES: https://topmate.io/coding/886874

Credits: https://t.me/free4unow_backup

ENJOY LEARNING 👍👍

Top 7 Must-Prepare Topics for Coding Interviews (2025 Edition)

✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc.
✅ Linked Lists – Practice reversal, cycle detection, and merging lists
✅ Hashing & Maps – Use hash tables for fast lookups and frequency-based problems
✅ Recursion & Backtracking – Solve problems like permutations, subsets, and Sudoku
✅ Dynamic Programming – Understand memoization, tabulation, and classic patterns
✅ Trees & Graphs – Cover traversal (BFS/DFS), shortest paths, and tree operations
✅ Stacks & Queues – Solve problems involving monotonic stacks, parentheses, and sliding windows

These are the essentials to crack FAANG-level interviews or product-based companies.

Join our WhatsApp channel before we reach 100k ❤️
👇
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

Tips for solving leetcode codings interview problems

If 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

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

Let's understand how Arrays & Strings can be asked in coding interviews

Arrays and strings are the building blocks of most coding interview problems. They test your logic, optimization skills, and your ability to recognize patterns — and they pop up in everything from system design to algorithm rounds.

*1.1. Rotation*

You may be asked to rotate an array left or right by k positions, in-place and with O(1) space.

Example:

> Rotate [1, 2, 3, 4, 5] right by 2 → Output: [4, 5, 1, 2, 3]

It tests how well you manage array indices and edge cases like k > n.

*1.2. Sliding Window*

Used to reduce brute-force O(n²) solutions to O(n). Interviewers love this for problems around subarrays, substrings, or fixed windows.

*Example* :

> Find the max sum of a subarray of size 3 in [4, 2, 1, 7, 8, 1, 2, 8, 1, 0] → Output: 17

It's commonly used in anagram detection, maximum subarray sum, and longest substring without repeating characters.


*1.3. Two Pointers*

Two indices scanning the array — from start and end or moving in sync. Great for reducing space/time complexity.

Example:

> Given [1, 2, 4, 4] and target = 8, return true if two numbers sum up to target → Output: True (4+4)

Common interview problems:

- Reverse a string/array

- Check for palindrome

- Remove duplicates in-place

- Merge two sorted arrays


*1.4. Prefix Sum*

Precompute cumulative sums to answer range queries in O(1) instead of O(n).

Example:

> For nums = [1, 2, 3, 4, 5], find sum from index 1 to 3 quickly → Output: 9 (2+3+4)



*Popular problems:*

- Subarray sum equals k
- Range sum queries
- Balanced subarrays


React with ❤️ once you're ready for the next concept Linked Lists

Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720

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Let's now move to next important concept asked in coding interviews: Linked Lists:

Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds.

2.1. Reverse a Linked List

Example:
Reverse this list:
1 → 2 → 3 → 4
Output: 4 → 3 → 2 → 1

Concept tested:
Rewiring the .next pointers — often asked with follow-ups like iterative vs. recursive solutions.

2.2. Detect Cycle in a Linked List

Example:
In 1 → 2 → 3 → 4 → 2 (back to second node), detect the cycle.

Solution:
Use Floyd’s Cycle Detection Algorithm (fast and slow pointers).

It tests how well you manage infinite loops and pointer traversal without modifying the list.

2.3. Merge Two Sorted Linked Lists

Example:
Merge 1 → 3 → 5 and 2 → 4 → 6
Output: 1 → 2 → 3 → 4 → 5 → 6

Concept tested:
Efficient pointer traversal with dummy nodes or recursion. A classic sub-task in linked list sorting.

2.4. Find the Middle of a Linked List

Example:
In 1 → 2 → 3 → 4 → 5 → 6, the middle node is 4.

Solution:
Fast and slow pointer — when the fast pointer reaches the end, the slow one is at the middle.

2.5. Remove N-th Node from End

Example:
Remove the 2nd node from the end of 1 → 2 → 3 → 4 → 5
Output: 1 → 2 → 3 → 5

Trick:
Create a gap of n between two pointers and move them together — when the first hits the end, the second is at the right spot.

You’ll see linked lists hidden in many real-world structures — like undo-redo functionality, LRU cache, or browser history stacks.


React with ❤️ once you're ready for the next concept Hashing & Maps

Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720

ENJOY LEARNING 👍👍

📩 Correct Way to Mail a Resume

Subject: Application For The [Role] at [Company Name]

Dear [Hiring Manager’s Name],

I hope you’re doing great. I came across the [Position Title] role at [Company Name] and was really excited about the opportunity to apply. With my experience in [mention key relevant experience], I believe I could bring value to your team.

I’ve attached my Resume for your review. I trust my background aligns with what you’re looking for, I’d love the chance to discuss how I can contribute to your team. Looking forward to hearing your thoughts!

Best regards,
[Your Name]
[Link To Linkedin]
[Link To Resume]

📩 Message to a Recruiter After Seeing Their Job Posting

Subject: Excited to Apply for [Position Title] at [Company Name]

Hi [Recruiter’s Name],

I trust you have a awesome day today 🙂
I just saw your post about the [Position Title] opening at [Company Name], and I couldn’t wait to reach out! I’ve been following [Company Name]
for a while now, and I truly admire [mention something specific—company’s projects, culture, values, recent achievements].

With my expertise in [mention relevant skills/experience], I believe I’d be a great fit for this role. I’ve attached my Resume for your review, and I’d love the chance to discuss how my experience can contribute to your team.

Would you be open to a quick chat?
Looking forward to your thoughts!

[Your Resume]

✉️ Warm Networking DM

Subject: Exploring Opportunities at [Company Name]

Hi [First Name],

I believe you have a wonderful day today 😊
I’m a [Your Role] specializing in [mention key skills]. I’ve been following [Company Name] for a while and love [mention something specific about their work, culture, or achievements].

With experience in [mention a key project or skill], I believe I could bring value to your team. If you’re open to it, I’d love to chat about any opportunities, where my skills could be a great fit.

I know you must get a ton of messages, so I really appreciate your time. Looking forward to hearing from you!

Warm,
[Your Name]
[Your Resume]