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Complete python notes..

10 Questions asked in Every Interview:>>>


1. Tell me about yourself.
2. Why are you interested in our company?
3. Why are you interested in this position?
4. What are your strengths?
5. What are your weaknesses?
6. Why should we hire you over other qualified candidates?
7. Where do you see yourself in 5 years?
8. Tell me about a time you failed.
9. What are your salary expectations?
10. What questions do you have for us?

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Clear all DSA rounds,

By mastering these 20 DSA patterns

1. Fast and Slow Pointer
- Cycle detection method
- O(1) space efficiency
- Linked list problems

2. Merge Intervals
- Sort and merge
- O(n log n) complexity
- Overlapping interval handling

3. Sliding Window
- Fixed/variable window
- O(n) time optimization
- Subarray/substring problems

4. Islands (Matrix Traversal)
- DFS/BFS traversal
- Connected component detection
- 2D grid problems

5. Two Pointers
- Dual pointer strategy
- Linear time complexity
- Array/list problems

6. Cyclic Sort
- Sorting in cycles
- O(n) time complexity
- Constant space usage

7. In-place Reversal of Linked List
- Reverse without extra space
- O(n) time efficiency
- Pointer manipulation technique

8. Breadth First Search
- Level-by-level traversal
- Uses queue structure
- Shortest path problems

9. Depth First Search
- Recursive/backtracking approach
- Uses stack (or recursion)
- Tree/graph traversal

10. Two Heaps
- Max and min heaps
- Median tracking efficiently
- O(log n) insertions

11. Subsets
- Generate all subsets
- Recursive or iterative
- Backtracking or bitmasking

12. Modified Binary Search
- Search in variations
- O(log n) time
- Rotated/specialized arrays

13. Bitwise XOR
- Toggle bits operation
- O(1) space complexity
- Efficient for pairing

14. Top 'K' elements
- Use heap/quickselect
- O(n log k) time
- Efficient selection problem

15. K-way Merge
- Merge sorted lists
- Min-heap based approach
- O(n log k) complexity

16. 0/1 Knapsack (Dynamic Programming)
- Choose or skip items
- O(n * W) complexity
- Maximize value selection

17. Unbounded Knapsack (Dynamic Programming)
- Unlimited item choices
- O(n * W) complexity
- Multiple item selection

18. Topological Sort (Graphs)
- Directed acyclic graph
- Order dependency resolution
- Uses DFS or BFS

19. Monotonic Stack
- Maintain increasing/decreasing stack
- Optimized for range queries
- O(n) time complexity

20. Backtracking
- Recursive decision-making
- Explore all possibilities
- Pruning with constraints

All the best 👍👍