20 Merge Sort Interview Questions and Answers

Merge Sort is a sorting algorithm that is used to sort an array of elements. This algorithm is based on the Divide and Conquer strategy, which means that it divides the array into smaller subarrays and then sorts each subarray. If you are applying for a position that requires knowledge of sorting algorithms, then you should expect to be asked questions about Merge Sort. In this article, we will review some common Merge Sort interview questions and how you can answer them.

Merge Sort Interview Questions and Answers

Here are 20 commonly asked Merge Sort interview questions and answers to prepare you for your interview:

1. What do you understand about merge sort?

Merge sort is a sorting algorithm that uses a divide and conquer strategy. It works by dividing an array into two halves, sorting each half, and then merging the two halves back together.

2. Can you explain how the divide and conquer strategy works with merge sort?

The divide and conquer strategy is a way of solving problems by breaking them down into smaller, more manageable pieces. With merge sort, this strategy is used by first dividing the array to be sorted into two smaller arrays. These arrays are then sorted individually, and finally merged back together into one sorted array.

3. How does merge sort compare to other sorting algorithms in terms of performance?

Merge sort is generally considered to be one of the more efficient sorting algorithms, particularly when compared to algorithms like selection sort or bubble sort. This is because merge sort is able to take advantage of the fact that it is working with already sorted sublists, which makes the overall sorting process much faster. Additionally, merge sort is a stable sorting algorithm, meaning that it preserves the order of equal elements in the list. This is not the case for all sorting algorithms, which can make merge sort a better choice in some situations.

4. Is it possible to use merge sort on a linked list? If yes, then how?

Yes, it is possible to use merge sort on a linked list. The way to do this is to first break the linked list into two smaller linked lists. Then, you can sort each of those smaller linked lists using merge sort. Finally, you can merge the two sorted lists back together to create a single sorted list.

5. How would you improve the performance of a merge sort algorithm?

One way to improve the performance of a merge sort algorithm is to use a more efficient sorting algorithm for the initial step of sorting the individual sublists. Another way to improve performance is to use a technique called “divide and conquer” which involves dividing the list into smaller sublists and then sorting those sublists in parallel.

6. How is merge sort different from insertion sort?

Merge sort is a divide and conquer algorithm that relies on recursion to sort a list, whereas insertion sort is an algorithm that sorts a list by repeatedly inserting elements into the correct position. Both algorithms have their own strengths and weaknesses, but in general, merge sort is more efficient for large lists while insertion sort is more efficient for small lists.

7. How is merge sort different from quick sort?

Merge sort is a sorting algorithm that uses a divide and conquer strategy. It starts by dividing the array into two halves, and then sorts each half recursively. Finally, it merges the two sorted halves back together. Quick sort is a sorting algorithm that uses a partitioning strategy. It starts by choosing a pivot element, and then partitions the array around that element. It then sorts the two partitions recursively.

8. Why is it important for us to have a stable sorting mechanism like merge sort?

A stable sorting algorithm is one where the elements that are equal maintain their original order with respect to each other. This is important because it means that we can sort a list of elements without having to worry about the order of the elements that are equal.

9. Can you give me some examples of when you would use merge sort instead of other algorithms?

Merge sort is a very versatile algorithm that can be used in a number of different situations. One common use case is when you need to sort a large amount of data that is too big to fit into memory all at once. Another common use case is when you need to sort data that is constantly changing and you can’t afford to do a full sort every time.

10. What is the best way to find out the number of times a particular element occurs in an array using merge sort?

The best way to find out the number of times a particular element occurs in an array using merge sort would be to keep track of the number of times the element occurs during the merge process.

11. What’s the worst case scenario for merge sort?

The worst case scenario for merge sort is when the array is already sorted in reverse order. In this case, the merge sort algorithm will need to make n-1 comparisons for each element in the array, resulting in a total of O(n^2) comparisons.

12. Can you give me some examples of problems that can be solved by using merge sort?

Merge sort can be used to solve a variety of problems, including sorting a list of numbers or objects in ascending or descending order, finding the median value in a list of numbers, and merging two or more sorted lists into one larger sorted list.

13. What are some ways to optimize merge sort?

There are a few ways to optimize merge sort. One way is to use a heuristic to try to avoid doing unnecessary work. Another way is to use a technique called “lazy update” which delays updates to the sorted array until absolutely necessary.

14. What is the space complexity of merge sort? Why is this such an issue with merge sort?

The space complexity of merge sort is O(n). This is because merge sort requires an extra array to store the elements while they are being sorted. This can be a big issue if the array is large, as it will take up a lot of memory.

15. Can you explain what tail recursion is? How is it useful?

Tail recursion is a type of recursion where the recursive call is the last thing that happens in the function. This is useful because it means that the function doesn’t need to keep track of any extra information – it can simply return the result of the recursive call. This can make tail recursive functions more efficient than other types of recursive functions.

16. What is meant by the “curse of dimensionality” in machine learning?

The curse of dimensionality is a term used to describe the problems that arise when working with data sets that have a large number of dimensions. When the number of dimensions is large, the data sets become very sparse and it becomes very difficult to find patterns. This can lead to problems such as overfitting, where the model only works well on the training data but not on new data.

17. What are the advantages of working with sorted data?

Sorted data is easier to work with because it is organized in a specific order. This makes it easier to find specific items, compare items, and perform other operations on the data. Additionally, sorted data can be processed more quickly because the computer can take advantage of the order to speed up the process.

18. What happens if we try to sort all the elements of a linked list at once?

If we try to sort all the elements of a linked list at once, we will end up with a lot of wasted space. This is because each node in the linked list will need to be sorted individually, and then the entire list will need to be sorted again as a whole. This will take up a lot of extra space and time, and is not very efficient.

19. What are the main issues involved with writing code for merge sort?

The main issues with writing code for merge sort are ensuring that the code correctly sorts the data, and that it does so in an efficient manner. To do this, the code must be able to correctly compare data values and then place them in the correct order. Additionally, the code must be able to efficiently handle large data sets.

20. Can you explain how merge sort relates to big O notation?

Merge sort is a sorting algorithm with a worst case and average case complexity of O(n log n). This means that, in the worst case scenario, the algorithm will take n log n time to sort a list of n elements. However, in the average case scenario, the algorithm will still take n log n time to sort the list.