✅ Top DSA Interview Questions with Answers: Part-3 🧠

21. What is the Sliding Window technique? 
It’s an optimization method used to reduce time complexity in problems involving arrays or strings. You create a "window" over a subset of data and slide it as needed, updating results on the go. 
Example use case: Find the maximum sum of any k consecutive elements in an array.

22. Explain the Two-Pointer technique. 
This involves using two indices (pointers) to traverse a data structure, usually from opposite ends or the same direction. It's helpful for searching pairs or reversing sequences efficiently. 
Common problems: Two-sum, palindrome check, sorted array partitioning.

23. What is the Binary Search algorithm? 
It’s an efficient algorithm to find an element in a sorted array by repeatedly dividing the search range in half. 
Time Complexity: O(log n) 
Key idea: Compare the target with the middle element and eliminate half the array each step.

24. What is the Merge Sort algorithm? 
A divide-and-conquer sorting algorithm that splits the array into halves, sorts them recursively, and then merges them. 
Time Complexity: O(n log n) 
Stable? Yes 
Extra space? Yes, due to merging.

25. What is the Quick Sort algorithm?
It chooses a pivot, partitions the array so elements < pivot are left, and > pivot are right, then recursively sorts both sides. 
Time Complexity: Avg – O(n log n), Worst – O(n²) 
Fast in practice, but not stable.

26. Difference between Merge Sort and Quick Sort 
- Merge Sort is stable, consistent in performance (O(n log n)), but uses extra space. 
- Quick Sort is faster in practice and works in-place, but may degrade to O(n²) if pivot is poorly chosen.

27. What is Insertion Sort and how does it work? 
It builds the sorted list one item at a time by comparing and inserting items into their correct position. 
Time Complexity: O(n²) 
Best Case (nearly sorted): O(n) 
Stable? Yes 
Space: O(1)

28. What is Selection Sort? 
It finds the smallest element from the unsorted part and swaps it with the beginning. 
Time Complexity: O(n²) 
Space: O(1) 
Stable? No 
Rarely used due to inefficiency.

29. What is Bubble Sort and its drawbacks? 
It repeatedly compares and swaps adjacent elements if out of order. 
Time Complexity: O(n²) 
Space: O(1) 
Drawback: Extremely slow for large data. Educational, not practical.

30. What is the time and space complexity of common sorting algorithms?
- Bubble Sort → Time: O(n²), Space: O(1), Stable: Yes 
- Selection Sort → Time: O(n²), Space: O(1), Stable: No 
- Insertion Sort → Time: O(n²), Space: O(1), Stable: Yes 
- Merge Sort → Time: O(n log n), Space: O(n), Stable: Yes 
- Quick Sort → Avg Time: O(n log n), Worst: O(n²), Space: O(log n), Stable: No

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