✅ 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
Double Tap ♥️ For Part-3
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
Double Tap ♥️ For Part-3
❤8
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗚𝗲𝘁 𝗛𝗶𝗴𝗵 𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝗜𝗻 𝟮𝟬𝟮𝟲😍
Opportunities With 500+ Hiring Partners
𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸:- https://pdlink.in/4hO7rWY
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/4fdWxJB
📈 Start learning today, build job-ready skills, and get placed in leading tech companies.
Opportunities With 500+ Hiring Partners
𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸:- https://pdlink.in/4hO7rWY
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/4fdWxJB
📈 Start learning today, build job-ready skills, and get placed in leading tech companies.
❤2
✅ 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
Double Tap ♥️ For More
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
Double Tap ♥️ For More
❤6
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗢𝗳𝗳𝗲𝗿𝗲𝗱 𝗕𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 & 𝗜𝗜𝗠 𝗠𝘂𝗺𝗯𝗮𝗶😍
Placement Assistance With 5000+ Companies
Deadline: 25th January 2026
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗜 :- https://pdlink.in/49UZfkX
𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:- https://pdlink.in/4pYWCEK
𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4tcUPia
Hurry..Up Only Limited Seats Available
Placement Assistance With 5000+ Companies
Deadline: 25th January 2026
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗜 :- https://pdlink.in/49UZfkX
𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:- https://pdlink.in/4pYWCEK
𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4tcUPia
Hurry..Up Only Limited Seats Available
❤1
✅ 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.
Double Tap ♥️ For More
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.
Double Tap ♥️ For More
❤1
✅ 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.
Double Tap ♥️ For More
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.
Double Tap ♥️ For More
❤4
𝗜𝗻𝗱𝗶𝗮’𝘀 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 | 𝗔𝗜 𝗜𝗺𝗽𝗮𝗰𝘁 𝗕𝘂𝗶𝗹𝗱𝗮𝘁𝗵𝗼𝗻😍
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
✅ JavaScript Practice Questions with Answers 💻⚡
🔍 Q1. How do you check if a number is even or odd?
🔍 Q2. How do you reverse a string?
🔍 Q3. Write a function to find the factorial of a number.
🔍 Q4. How do you remove duplicates from an array?
🔍 Q5. Print numbers from 1 to 10 using a loop.
🔍 Q6. Check if a word is a palindrome.
💬 Tap ❤️ for more!
🔍 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");
}
💬 Tap ❤️ for more!
❤4👍1
🚀 𝟰 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟲 😍
📈 Upgrade your career with in-demand tech skills & FREE certifications!
1️⃣ AI & ML – https://pdlink.in/4bhetTu
2️⃣ Data Analytics – https://pdlink.in/497MMLw
3️⃣ Cloud Computing – https://pdlink.in/3LoutZd
4️⃣ Cyber Security – https://pdlink.in/3N9VOyW
More Courses – https://pdlink.in/4qgtrxU
🎓 100% FREE | Certificates Provided | Learn Anytime, Anywhere
📈 Upgrade your career with in-demand tech skills & FREE certifications!
1️⃣ AI & ML – https://pdlink.in/4bhetTu
2️⃣ Data Analytics – https://pdlink.in/497MMLw
3️⃣ Cloud Computing – https://pdlink.in/3LoutZd
4️⃣ Cyber Security – https://pdlink.in/3N9VOyW
More Courses – https://pdlink.in/4qgtrxU
🎓 100% FREE | Certificates Provided | Learn Anytime, Anywhere
✅ 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:
📍 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
📍 6. Explain 'this' keyword in JavaScript.
Answer:
📍 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:
📍 9. How do you handle errors in JavaScript?
Answer: Using
📍 🔟 What are modules in JavaScript and their benefits?
Answer: Modules split code into reusable files with
💡 Pro Tip: Complement your answers with simple code snippets and real project scenarios wherever possible.
❤️ Tap for more!
📍 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.
❤️ Tap for more!
❤5
𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 😍
* JAVA- Full Stack Development With Gen AI
* MERN- Full Stack Development With Gen AI
Highlightes:-
* 2000+ Students Placed
* Attend FREE Hiring Drives at our Skill Centres
* Learn from India's Best Mentors
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-
https://pdlink.in/4hO7rWY
Hurry, limited seats available!
* JAVA- Full Stack Development With Gen AI
* MERN- Full Stack Development With Gen AI
Highlightes:-
* 2000+ Students Placed
* Attend FREE Hiring Drives at our Skill Centres
* Learn from India's Best Mentors
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-
https://pdlink.in/4hO7rWY
Hurry, limited seats available!
If you want to Excel at Web Development and build stunning websites, master these essential skills:
Frontend:
• HTML, CSS, JavaScript – Core web technologies
• Flexbox & Grid – Master modern CSS layouts
• Responsive Design – Make websites mobile-friendly
• JavaScript ES6+ – Arrow functions, Promises, Async/Await
• React, Vue, or Angular – Modern frontend frameworks
• APIs & Fetch/Axios – Connect frontend with backend
• State Management – Redux, Vuex, or Context API
Backend:
• Node.js & Express.js – Build powerful server-side applications
• Databases – MySQL, PostgreSQL, MongoDB (NoSQL)
• RESTful APIs & GraphQL – Handle data efficiently
• Authentication – JWT, OAuth, and session management
• WebSockets – Real-time applications
DevOps & Deployment:
• Version Control – Git & GitHub
• CI/CD Pipelines – Automate deployments
• Cloud Hosting – AWS, Firebase, Vercel, Netlify
• Docker & Kubernetes – Scalable applications
Like it if you need a complete tutorial on all these topics! 👍❤️
Frontend:
• HTML, CSS, JavaScript – Core web technologies
• Flexbox & Grid – Master modern CSS layouts
• Responsive Design – Make websites mobile-friendly
• JavaScript ES6+ – Arrow functions, Promises, Async/Await
• React, Vue, or Angular – Modern frontend frameworks
• APIs & Fetch/Axios – Connect frontend with backend
• State Management – Redux, Vuex, or Context API
Backend:
• Node.js & Express.js – Build powerful server-side applications
• Databases – MySQL, PostgreSQL, MongoDB (NoSQL)
• RESTful APIs & GraphQL – Handle data efficiently
• Authentication – JWT, OAuth, and session management
• WebSockets – Real-time applications
DevOps & Deployment:
• Version Control – Git & GitHub
• CI/CD Pipelines – Automate deployments
• Cloud Hosting – AWS, Firebase, Vercel, Netlify
• Docker & Kubernetes – Scalable applications
Like it if you need a complete tutorial on all these topics! 👍❤️
❤2
🚀 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻
Placement Assistance With 5000+ companies.
✅ Open to everyone
✅ 100% Online | 6 Months
✅ Industry-ready curriculum
✅ Taught By IIT Roorkee Professors
🔥 Companies are actively hiring candidates with Data Science & AI skills.
⏳ Deadline: 31st January 2026
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇 :-
https://pdlink.in/49UZfkX
✅ Limited seats only
Placement Assistance With 5000+ companies.
✅ Open to everyone
✅ 100% Online | 6 Months
✅ Industry-ready curriculum
✅ Taught By IIT Roorkee Professors
🔥 Companies are actively hiring candidates with Data Science & AI skills.
⏳ Deadline: 31st January 2026
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇 :-
https://pdlink.in/49UZfkX
✅ Limited seats only
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.....
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
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.....
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
❤2
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
React ❤️ for more
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
React ❤️ for more
❤3
🚀 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗪𝗶𝘁𝗵 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 (𝗘&𝗜𝗖𝗧 𝗔𝗰𝗮𝗱𝗲𝗺𝘆)
Get guidance from IIT Roorkee experts and become job-ready for top tech roles.
✅ Open to all graduates & students
✅ Industry-focused curriculum
✅ Online learning flexibility
✅ Placement Assistance With 5000+ Companies
💼 Companies are hiring candidates with strong Software Engineering skills!
𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸👇:
https://pdlink.in/4pYWCEK
⏳ Don’t miss this opportunity to upskill with IIT Roorkee.
Get guidance from IIT Roorkee experts and become job-ready for top tech roles.
✅ Open to all graduates & students
✅ Industry-focused curriculum
✅ Online learning flexibility
✅ Placement Assistance With 5000+ Companies
💼 Companies are hiring candidates with strong Software Engineering skills!
𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸👇:
https://pdlink.in/4pYWCEK
⏳ Don’t miss this opportunity to upskill with IIT Roorkee.
𝗛𝗼𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗮𝘀𝘁 (𝗘𝘃𝗲𝗻 𝗜𝗳 𝗬𝗼𝘂'𝘃𝗲 𝗡𝗲𝘃𝗲𝗿 𝗖𝗼𝗱𝗲𝗱 𝗕𝗲𝗳𝗼𝗿𝗲!)🐍🚀
Python is everywhere—web dev, data science, automation, AI…
But where should YOU start if you're a beginner?
Don’t worry. Here’s a 6-step roadmap to master Python the smart way (no fluff, just action)👇
🔹 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
Libraries are what make Python powerful. Learn one at a time with a mini project.
🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev)
✅ Track your code with Git
✅ Upload projects to GitHub
✅ Write clear README files
✅ Contribute to open source repos
Your GitHub profile = Your online CV. Keep it active!
🔹 𝗦𝘁𝗲𝗽 𝟲: Build a Capstone Project (Level-Up!)
✅ A weather dashboard (API + Flask)
✅ A personal expense tracker
✅ A web scraper that sends email alerts
✅ A basic portfolio website in Python + Flask
Pick something that solves a real problem—bonus if it helps you in daily life!
🎯 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 = 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴
You don’t need to memorize code. Understand the logic.
Google is your best friend. Practice is your real teacher.
Python Resources: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
ENJOY LEARNING 👍👍
Python is everywhere—web dev, data science, automation, AI…
But where should YOU start if you're a beginner?
Don’t worry. Here’s a 6-step roadmap to master Python the smart way (no fluff, just action)👇
🔹 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
Libraries are what make Python powerful. Learn one at a time with a mini project.
🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev)
✅ Track your code with Git
✅ Upload projects to GitHub
✅ Write clear README files
✅ Contribute to open source repos
Your GitHub profile = Your online CV. Keep it active!
🔹 𝗦𝘁𝗲𝗽 𝟲: Build a Capstone Project (Level-Up!)
✅ A weather dashboard (API + Flask)
✅ A personal expense tracker
✅ A web scraper that sends email alerts
✅ A basic portfolio website in Python + Flask
Pick something that solves a real problem—bonus if it helps you in daily life!
🎯 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 = 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴
You don’t need to memorize code. Understand the logic.
Google is your best friend. Practice is your real teacher.
Python Resources: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
ENJOY LEARNING 👍👍
❤1
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺😍
Master in-demand tools like Python, SQL, Excel, Power BI, and Machine Learning while working on real-time projects.
🎯 Beginner to Advanced Level
💼 Placement Assistance with Top Hiring Partners
📁 Real-world Case Studies & Capstone Projects
📜 Industry-recognized Certification
💰 High Salary Career Path in Analytics & Data Science
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇:-
https://pdlink.in/4fdWxJB
( Hurry Up 🏃♂️Limited Slots )
Master in-demand tools like Python, SQL, Excel, Power BI, and Machine Learning while working on real-time projects.
🎯 Beginner to Advanced Level
💼 Placement Assistance with Top Hiring Partners
📁 Real-world Case Studies & Capstone Projects
📜 Industry-recognized Certification
💰 High Salary Career Path in Analytics & Data Science
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄 👇:-
https://pdlink.in/4fdWxJB
( Hurry Up 🏃♂️Limited Slots )
❤1
✅𝗖𝗼𝗿𝗿𝗲𝗰𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗮𝘀𝗸 𝗳𝗼𝗿 𝗮 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹:👩💻
---
Subject: Referral Request for [Position] at [Company Name]
Hi [Recipient's Name]🙂,
I hope you’re doing well. I’m interested in the [Position] at [Company] and noticed you work there. My background in data analytics, particularly in [specific expertise], aligns well with this role.
I understand the interviews will likely focus heavily on technical data analysis skills, and I’m well-prepared, having worked on numerous projects and effectively used data-driven strategies to address complex challenges.
Here are the details for your reference:
- Job posting: [Job Link]
- Resume: [Resume Link]
- Projects and coding profile:
- GitHub: [GitHub Link]
- [Coding Profile Link] (e.g., [mention ranking/level if impressive])
I assure you that a referral will be highly valued and I will make the most of this opportunity. I’m also happy to assist you with anything in return.
Any additional suggestion/advice you can provide would be greatly appreciated.
Thanks in advance!
Best,
[Your Full Name]
---
Subject: Referral Request for [Position] at [Company Name]
Hi [Recipient's Name]🙂,
I hope you’re doing well. I’m interested in the [Position] at [Company] and noticed you work there. My background in data analytics, particularly in [specific expertise], aligns well with this role.
I understand the interviews will likely focus heavily on technical data analysis skills, and I’m well-prepared, having worked on numerous projects and effectively used data-driven strategies to address complex challenges.
Here are the details for your reference:
- Job posting: [Job Link]
- Resume: [Resume Link]
- Projects and coding profile:
- GitHub: [GitHub Link]
- [Coding Profile Link] (e.g., [mention ranking/level if impressive])
I assure you that a referral will be highly valued and I will make the most of this opportunity. I’m also happy to assist you with anything in return.
Any additional suggestion/advice you can provide would be greatly appreciated.
Thanks in advance!
Best,
[Your Full Name]
❤2
✅ Coding Interview Acronyms You MUST Know 💻🔥
DSA → Data Structures & Algorithms
CPU → Central Processing Unit
RAM → Random Access Memory
DBMS → Database Management System
RDBMS → Relational Database Management System
ACID → Atomicity, Consistency, Isolation, Durability
OLTP → Online Transaction Processing
OLAP → Online Analytical Processing
TCP → Transmission Control Protocol
IP → Internet Protocol
DNS → Domain Name System
MVC → Model View Controller
MVVM → Model View ViewModel
SDLC → Software Development Life Cycle
CI/CD → Continuous Integration / Continuous Deployment
JWT → JSON Web Token
ORM → Object Relational Mapping
API → Application Programming Interface
REST → Representational State Transfer
SOAP → Simple Object Access Protocol
Big O → Time & Space Complexity Notation
FIFO → First In First Out
LIFO → Last In First Out
💬 Double Tap ❤️ for more!
DSA → Data Structures & Algorithms
CPU → Central Processing Unit
RAM → Random Access Memory
DBMS → Database Management System
RDBMS → Relational Database Management System
ACID → Atomicity, Consistency, Isolation, Durability
OLTP → Online Transaction Processing
OLAP → Online Analytical Processing
TCP → Transmission Control Protocol
IP → Internet Protocol
DNS → Domain Name System
MVC → Model View Controller
MVVM → Model View ViewModel
SDLC → Software Development Life Cycle
CI/CD → Continuous Integration / Continuous Deployment
JWT → JSON Web Token
ORM → Object Relational Mapping
API → Application Programming Interface
REST → Representational State Transfer
SOAP → Simple Object Access Protocol
Big O → Time & Space Complexity Notation
FIFO → First In First Out
LIFO → Last In First Out
💬 Double Tap ❤️ for more!
❤8