Exploring Sorting Algorithms

What is the primary purpose of sorting algorithms?

• To arrange data in a specific order.

• To delete unnecessary data.

• To encrypt sensitive information.

• To increase the size of data sets.

Name two common sorting algorithms.

• Bubble Sort, Quick Sort

• Insertion Sort

• Merge Sort

• Selection Sort

What is the time complexity of Bubble Sort in the worst case?

• O(log n)

• O(n^2)

• O(n log n)

• O(n)

How does Quick Sort choose its pivot element?

• Quick Sort uses the median of all elements as the pivot.

• Quick Sort chooses its pivot element based on various strategies, such as first, last, middle, or random element.

• Quick Sort selects the pivot based on the size of the array only.

• Quick Sort always chooses the first element as the pivot.

What is the difference between stable and unstable sorting algorithms?

• Unstable sorting algorithms can only sort numbers.

• Stable sorting algorithms require more memory than unstable ones.

• Stable sorting algorithms preserve the order of equal elements; unstable sorting algorithms do not.

• Stable sorting algorithms are faster than unstable ones.

Which sorting algorithm is generally considered the fastest for large datasets?

• BubbleSort

• InsertionSort

• SelectionSort

• QuickSort

What is the main advantage of Merge Sort over other sorting algorithms?

• It is easier to implement than Bubble Sort.

• It requires less memory than Quick Sort.

• Its consistent O(n log n) time complexity in all cases.

• It is the fastest sorting algorithm for small datasets.

Explain how Insertion Sort works.

• Insertion Sort sorts the array by repeatedly swapping adjacent elements.

• Insertion Sort is a divide-and-conquer algorithm that splits the array into halves.

• Insertion Sort requires a pre-sorted array to function effectively.

• Insertion Sort is a simple sorting algorithm that builds the final sorted array one item at a time.

What is the best-case time complexity of Selection Sort?

• O(n)

• O(n log n)

• O(n^2)

• O(log n)

In which scenarios would you prefer using a counting sort?

• When the range of input values is small relative to the number of elements to be sorted.

• When the input values are highly diverse and numerous.

• When the algorithm must handle negative numbers efficiently.

• When the sorting needs to be stable and maintain original order.