✅ Top 50 JavaScript Interview Questions 💻✨

1. What are the key features of JavaScript?
2. Difference between var, let, and const
3. What is hoisting?
4. Explain closures with an example
5. What is the difference between == and ===?
6. What is event bubbling and capturing?
7. What is the DOM?
8. Difference between null and undefined
9. What are arrow functions?
10. Explain callback functions
11. What is a promise in JS?
12. Explain async/await
13. What is the difference between call, apply, and bind?
14. What is a prototype?
15. What is prototypal inheritance?
16. What is the use of ‘this’ keyword in JS?
17. Explain the concept of scope in JS
18. What is lexical scope?
19. What are higher-order functions?
20. What is a pure function?
21. What is the event loop in JS?
22. Explain microtask vs. macrotask queue
23. What is JSON and how is it used?
24. What are IIFEs (Immediately Invoked Function Expressions)?
25. What is the difference between synchronous and asynchronous code?
26. How does JavaScript handle memory management?
27. What is a JavaScript engine?
28. Difference between deep copy and shallow copy in JS
29. What is destructuring in ES6?
30. What is a spread operator?
31. What is a rest parameter?
32. What are template literals?
33. What is a module in JS?
34. Difference between default export and named export
35. How do you handle errors in JavaScript?
36. What is the use of try...catch?
37. What is a service worker?
38. What is localStorage vs. sessionStorage?
39. What is debounce and throttle?
40. Explain the fetch API
41. What are async generators?
42. How to create and dispatch custom events?
43. What is CORS in JS?
44. What is memory leak and how to prevent it in JS?
45. How do arrow functions differ from regular functions?
46. What are Map and Set in JavaScript?
47. Explain WeakMap and WeakSet
48. What are symbols in JS?
49. What is functional programming in JS?
50. How do you debug JavaScript code?

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✅ Python for Data Science – Part 1: NumPy Interview Q&A 📊

🔹 1. What is NumPy and why is it important?
NumPy (Numerical Python) is a powerful Python library for numerical computing. It supports fast array operations, broadcasting, linear algebra, and random number generation. It’s the backbone of many data science libraries like Pandas and Scikit-learn.

🔹 2. Difference between Python list and NumPy array
Python lists can store mixed data types and are slower for numerical operations. NumPy arrays are faster, use less memory, and support vectorized operations, making them ideal for numerical tasks.

🔹 3. How to create a NumPy array

import numpy as np
arr = np.array([1, 2, 3])

🔹 4. What is broadcasting in NumPy?
Broadcasting lets you perform operations on arrays of different shapes. For example, adding a scalar to an array applies the operation to each element.

🔹 5. How to generate random numbers
Use np.random.rand() for uniform distribution, np.random.randn() for normal distribution, and np.random.randint() for random integers.

🔹 6. How to reshape an array
Use .reshape() to change the shape of an array without changing its data.
Example: arr.reshape(2, 3) turns a 1D array of 6 elements into a 2x3 matrix.

🔹 7. Basic statistical operations
Use functions like mean(), std(), var(), sum(), min(), and max() to get quick stats from your data.

🔹 8. Difference between zeros(), ones(), and empty()
np.zeros() creates an array filled with 0s, np.ones() with 1s, and np.empty() creates an array without initializing values (faster but unpredictable).

🔹 9. Handling missing values
Use np.nan to represent missing values and np.isnan() to detect them.
Example:

arr = np.array([1, 2, np.nan])
np.isnan(arr)  # Output: [False False True]

🔹 10. Element-wise operations
NumPy supports element-wise addition, subtraction, multiplication, and division.
Example:

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
a + b  # Output: [5 7 9]

💡 Pro Tip: NumPy is all about speed and efficiency. Mastering it gives you a huge edge in data manipulation and model building.

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✅ Coding Interview Questions with Answers Part-2 🧠💻

11. What is a hash table? How hashing works
A hash table stores key-value pairs. It uses a hash function to map keys to an index.
• Key goes into hash function
• Hash function returns index
• Value stores at that index
Why interviewers like it:
• Average lookup time is O(1)
• Used in caching and indexing
Real examples:
• Python dict
• Java HashMap

12. What are collisions in hashing? How to handle them
Collision happens when two keys map to the same index.
Common handling methods:
• Chaining: Each index holds a linked list
• Open addressing: Find next empty slot
Types of open addressing:
• Linear probing
• Quadratic probing
• Double hashing
Interview tip:
• Chaining is easier to explain
• Worst case becomes O(n)

13. Difference between HashMap and HashSet
HashMap:
• Stores key and value
• Keys are unique
• Values duplicate allowed
HashSet:
• Stores only keys
• No duplicates
• Internally uses HashMap
Use cases:
• HashMap for lookup with data
• HashSet for uniqueness checks

14. What is a binary tree
A binary tree is a tree where each node has at most two children: left child and right child.
Common types:
• Full binary tree
• Complete binary tree
• Perfect binary tree
Uses:
• Hierarchical data
• Expression trees

15. What is a binary search tree
A binary search tree follows ordering rules.
Rules:
• Left child < root
• Right child > root
Operations:
• Search, insert, delete in O(log n) average
• Worst case O(n) if unbalanced
Interview focus:
• Inorder traversal gives sorted output

16. Difference between BFS and DFS
BFS:
• Level by level
• Uses queue
• Finds shortest path
DFS:
• Goes deep first
• Uses stack or recursion
• Uses less memory
When to use:
• BFS for shortest path
• DFS for traversal problems

17. What is a balanced tree
A balanced tree keeps height minimal.
Why it matters:
• Operations stay O(log n)
• Prevents skewed structure
Examples:
• AVL tree
• Red-Black tree
Interview note:
• Balance improves performance

18. What is heap data structure
Heap is a complete binary tree. It follows heap property.
Properties:
• Complete tree
• Parent follows order rule
Common use:
• Priority queues
• Scheduling tasks
Time complexity:
• Insert and delete take O(log n)

19. Difference between min heap and max heap
Min heap:
• Root holds smallest value
• Used when smallest priority wins
Max heap:
• Root holds largest value
• Used when highest priority wins
Example:
• Job scheduling
• Top K problems

20. What is a graph? Directed vs undirected
Graph contains nodes and edges.
Directed graph:
• Edges have direction
• One-way relationship
Undirected graph:
• No direction
• Two-way relationship
Real examples:
• Directed: Twitter follow
• Undirected: Facebook friends

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✅ Coding Interview Questions with Answers Part-3 🧠💻

21. Adjacency matrix vs adjacency list
Adjacency matrix:
• 2D array
• Space O(V²)
• Fast edge lookup
• Poor for sparse graphs
Adjacency list:
• List of neighbors
• Space O(V + E)
• Better for sparse graphs
• Used in real systems
Interview rule:
• Choose list unless graph is dense

22. What is sorting? Common sorting algorithms
Sorting arranges data in order.
Common algorithms:
• Bubble sort
• Selection sort
• Insertion sort
• Merge sort
• Quick sort
• Heap sort
Why it matters:
• Improves searching
• Simplifies data processing

23. Difference between quick sort and merge sort
Quick sort:
• Divide and conquer
• In-place
• Average O(n log n)
• Worst O(n²)
Merge sort:
• Divide and conquer
• Extra memory needed
• Always O(n log n)
• Stable
Interview pick:
• Quick sort for speed
• Merge sort for consistency

24. Which sorting algorithm is fastest and why
No single fastest algorithm.
General rules:
• Quick sort for average cases
• Merge sort for guaranteed performance
• Heap sort for memory control
Built-in sorts:
• Python uses Timsort
• Optimized for real data
Interview line:
• Depends on data and constraints

25. What is searching? Linear vs binary search
Searching finds an element.
Linear search:
• Checks one by one
• Time O(n)
• Works on any data
Binary search:
• Splits data
• Time O(log n)
• Needs sorted data

26. Why binary search needs sorted data
Binary search relies on order.
Reason:
• Decides left or right
• Without order, logic fails
Example:
• Phone book search
• Sorted arrays
Key point:
• Sorting enables efficiency

27. What is dynamic programming
Dynamic programming solves problems by storing results.
Core ideas:
• Overlapping subproblems
• Optimal substructure
Approaches:
• Top-down with memoization
• Bottom-up with tabulation
Classic problems:
• Fibonacci
• Knapsack
• Longest common subsequence

28. Greedy vs dynamic programming
Greedy:
• Takes local best
• Fast
• Not always correct
Dynamic programming:
• Considers all possibilities
• Slower
• Guarantees optimal result
Example:
• Coin change fails with greedy
• Works with dynamic programming

29. What is memoization
Memoization stores function results.
Purpose:
• Avoid recomputation
• Reduce time complexity
Example:
• Recursive Fibonacci with cache
Interview tip:
• Memoization trades memory for speed

30. What is backtracking
Backtracking explores all choices.
Steps:
• Choose
• Explore
• Undo
Used in:
• N-Queens
• Sudoku
• Permutations
Interview focus:
• Pruning reduces search space

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✅ Coding Interview Questions with Answers Part-4 🧠💻

31. What is a pointer
A pointer stores the memory address of a variable.
- Direct memory access
- Efficient data handling
Used in C/C++, dynamic memory allocation.

32. Difference between pointer and reference
Pointer: holds address, can be null, can change target.
Reference: alias of variable, cannot be null, cannot change binding.
Pointer is flexible, reference is safer.

33. What is a memory leak
Memory leak: memory not released.
- Program slows down
- App crashes
Causes: missing free/delete, unclosed resources.
Use garbage collection or smart pointers.

34. What is segmentation fault
Segmentation fault: invalid memory access.
- Access null pointer
- Array index overflow
Seen in C/C++. Check pointer usage.

35. Difference between process and thread
Process: independent, own memory, heavier.
Thread: part of process, shared memory, lightweight.
Threads share resources.

36. What is multithreading
Multithreading: runs tasks in parallel.
- Better CPU use
- Faster execution
Examples: web servers, background tasks.
Risk: data inconsistency.

37. What is synchronization
Synchronization: controls shared data access.
Prevents race conditions. Use locks, mutex, semaphores.
Safety over speed.

38. What is deadlock
Deadlock: threads wait forever.
- Thread A holds lock 1
- Thread B holds lock 2
Result: system freeze.

39. Conditions for deadlock
All four must exist:
- Mutual exclusion
- Hold and wait
- No preemption
- Circular wait
Break one to avoid deadlock.

40. Shallow copy vs deep copy
Shallow: copies reference, changes affect both.
Deep: copies data, fully independent.
Example: objects with nested data.

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✅ Coding Interview Questions with Answers Part-5 🧠💻

41. What is exception handling
Exception handling manages runtime errors.
• Prevents crashes
• Keeps program running
Try-catch-finally blocks handle errors (e.g., divide by zero).

42. Checked vs unchecked exceptions
Checked: compile-time, must handle (e.g., IOException).
Unchecked: runtime, not forced (e.g., NullPointerException).
Checked equals predictable.

43. Mutable vs immutable object
Mutable: state changes (e.g., List).
Immutable: state fixed (e.g., String).
Immutability provides thread safety and easier debugging.

44. What is garbage collection
Garbage collection frees unused memory.
Tracks unreachable objects and deletes them (Java, Python).
Reduces memory leaks.

45. What is REST API
REST API enables client-server communication via HTTP methods (GET, POST, PUT, DELETE).
Stateless requests (e.g., fetch user data).

46. What is JSON
JSON: lightweight, readable data format.
Language-independent, used in API responses (key-value pairs).

47. HTTP vs HTTPS
HTTP: no encryption, less secure.
HTTPS: encrypted, protects user data.

48. What is version control? Why Git matters
Version control tracks code changes. Git enables collaboration, rollback, and branch management.
Used by all tech teams.

49. Explain a coding problem you optimized recently
State problem, bottleneck, optimization, and result (e.g., reduced nested loops, improved O(n²) to O(n)).

50. How you approach a new coding problem
1. Understand problem
2. Clarify constraints
3. Brute force
4. Optimize
5. Test edge cases

Think aloud; interviewers watch process.

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✅ JavaScript Practice Questions with Answers 💻⚡

🔍 Q1. How do you check if a number is even or odd?

let num = 10;
if (num % 2 === 0) {
  console.log("Even");
} else {
  console.log("Odd");
}

🔍 Q2. How do you reverse a string?

let text = "hello";
let reversedText = text.split("").reverse().join("");
console.log(reversedText);  // Output: olleh

🔍 Q3. Write a function to find the factorial of a number.

function factorial(n) {
  let result = 1;
  for (let i = 1; i <= n; i++) {
    result *= i;
  }
  return result;
}
console.log(factorial(5));  // Output: 120

🔍 Q4. How do you remove duplicates from an array?

let items = [1, 2, 2, 3, 4, 4];
let uniqueItems = [...new Set(items)];
console.log(uniqueItems);

🔍 Q5. Print numbers from 1 to 10 using a loop.

for (let i = 1; i <= 10; i++) {
  console.log(i);
}

🔍 Q6. Check if a word is a palindrome.

let word = "madam";
let reversed = word.split("").reverse().join("");
if (word === reversed) {
  console.log("Palindrome");
} else {
  console.log("Not a palindrome");
}

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✅ Top JavaScript Interview Questions & Answers 💻✨

📍 1. What is JavaScript and why is it important?
Answer: JavaScript is a dynamic, interpreted programming language that makes web pages interactive. It runs in browsers and on servers (Node.js), enabling features like animations, form validation, and API calls.

📍 2. Explain the difference between var, let, and const.
Answer: var has function scope and is hoisted; let and const have block scope. const defines constants and cannot be reassigned.

📍 3. What are closures in JavaScript?
Answer: Closures occur when a function remembers and accesses variables from its outer scope even after that outer function has finished executing.

📍 4. What is the Event Loop?
Answer: The Event Loop manages asynchronous callbacks by pulling tasks from the callback queue and executing them after the call stack is empty, enabling non-blocking code.

📍 5. What are Promises and how do they help?
Answer: Promises represent the eventual completion or failure of an asynchronous operation, allowing cleaner async code with .then(), .catch(), and async/await.

📍 6. Explain 'this' keyword in JavaScript.
Answer: this refers to the context object in which the current function is executed — it varies in global, object, class, or arrow function contexts.

📍 7. What is prototypal inheritance?
Answer: Objects inherit properties and methods from a prototype object, allowing reuse and shared behavior in JavaScript.

📍 8. Difference between == and === operators?
Answer: == compares values after type coercion; === compares both value and type strictly.

📍 9. How do you handle errors in JavaScript?
Answer: Using try...catch blocks for synchronous code and .catch() or try-catch with async/await for asynchronous errors.

📍 🔟 What are modules in JavaScript and their benefits?
Answer: Modules split code into reusable files with import and export. They improve maintainability and scope management.

💡 Pro Tip: Complement your answers with simple code snippets and real project scenarios wherever possible.

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• Flexbox & Grid – Master modern CSS layouts
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🔰 Python Set Methods

Here are some interview preparation tips 👇👇

Technical Interview
1. Review Core Concepts:
  - Data Structures: Be comfortable with LinkedLists, Trees, Graphs, and their representations.
  - Algorithms: Brush up on searching and sorting algorithms, time complexities, and common algorithms (like Dijkstra’s or A*).
  - Programming Languages: Ensure you understand the language you are most comfortable with (e.g., C++, Java, Python) and know its standard library functions.

2. Practice Coding Problems:
  - Utilize platforms like LeetCode, HackerRank, or CodeSignal to practice medium-level coding questions. Focus on common patterns and problem-solving strategies.

3. Mock Interviews: Conduct mock technical interviews with peers or mentors to build confidence and receive feedback.

Personal Interview
1. Prepare Your Story:
  - Outline your educational journey, achievements, and any relevant projects. Emphasize experiences that demonstrate leadership, teamwork, and problem-solving skills.
  - Be ready to discuss your challenges and how you overcame them.

2. Articulate Your Goals:
  - Be clear about why you want to join the program and how it aligns with your career aspirations. Reflect on what you hope to gain from the experience.

- Focus on Fundamentals:
Be thorough with basic subjects like Operating Systems, Networking, OOP, and Databases. Clear concepts are key for technical interviews.

2. Common Interview Questions:

DSA:
- Implement various data structures like Linked Lists, Trees, Graphs, Stacks, and Queues.
- Understand searching and sorting algorithms: Binary Search, Merge Sort, Quick Sort, etc.
- Solve problems involving HashMaps, Sets, and other collections.

Sample DSA Questions
- Reverse a linked list.
- Find the first non-repeating character in a string.
- Detect a cycle in a graph.
- Implement a queue using two stacks.
- Find the lowest common ancestor in a binary tree.
 
3. Key Topics to Focus On

DSA:
- Arrays, Strings, Linked Lists, Trees, Graphs
- Recursion, Backtracking, Dynamic Programming
- Sorting and Searching Algorithms
- Time and Space Complexity

Core Subjects
- Operating Systems: Concepts like processes, threads, deadlocks, concurrency, and memory management.
- Database Management Systems (DBMS): Understanding SQL, Normalization, and database design.
- Object-Oriented Programming (OOP): Know about inheritance, polymorphism, encapsulation, and design patterns.
 
5. Tips
- Optimize Your Code: Write clean, optimized code. Discuss time and space complexities during interviews.
- Review Your Projects: Be ready to explain your past projects, the challenges you faced, and the technologies you used.....

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SQL Interview Questions with Answers

1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.

2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.

3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.

4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

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🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics (Don’t Skip This!)
✅ Variables, data types (int, float, string, bool)
✅ Loops (for, while), conditionals (if/else)
✅ Functions and user input
Start with:
Python.org Docs
YouTube: Programming with Mosh / CodeWithHarry
Platforms: W3Schools / SoloLearn / FreeCodeCamp
Spend a week here.

Practice > Theory.

🔹 𝗦𝘁𝗲𝗽 𝟮: Automate Boring Stuff (It’s Fun + Useful!)
✅ Rename files in bulk
✅ Auto-fill forms
✅ Web scraping with BeautifulSoup or Selenium
Read: “Automate the Boring Stuff with Python”
It’s beginner-friendly and practical!

🔹 𝗦𝘁𝗲𝗽 𝟯: Build Mini Projects (Your Confidence Booster)
✅ Calculator app
✅ Dice roll simulator
✅ Password generator
✅ Number guessing game

These small projects teach logic, problem-solving, and syntax in action.

🔹 𝗦𝘁𝗲𝗽 𝟰: Dive Into Libraries (Python’s Superpower)
✅ Pandas and NumPy – for data
✅ Matplotlib – for visualizations
✅ Requests – for APIs
✅ Tkinter – for GUI apps
✅ Flask – for web apps

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🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev)
✅ Track your code with Git
✅ Upload projects to GitHub
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✅ A weather dashboard (API + Flask)
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✅ A basic portfolio website in Python + Flask

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You don’t need to memorize code. Understand the logic.
Google is your best friend. Practice is your real teacher.

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