Test: Searching and Sorting - 1 - Software Development MCQ

Linear search, also known as sequential search, checks each element of the list one by one until the target element is found or the end of the list is reached.

Merge sort has a time complexity of O(n log n) in the average case, making it more efficient than other sorting algorithms.

Quick sort is an in-place sorting algorithm, meaning it doesn't require additional memory to perform the sorting operation.

Quick sort has a smaller memory footprint compared to merge sort as it uses in-place partitioning to sort the elements.

Insertion sort performs well when the data is already partially sorted because it efficiently places elements in their correct positions by comparing and shifting adjacent elements.

Bubble sort has a worst-case time complexity of O(n²), where n is the number of elements in the array. It occurs when the array is in reverse order.

Merge sort has a time complexity of O(n log n) in the worst-case scenario. It divides the array into halves recursively and then merges them, resulting in a logarithmic time complexity.

Bubble sort is an example of an unstable sorting algorithm. Unstable sorting algorithms may change the relative order of equal elements during the sorting process, while stable sorting algorithms preserve the order.

Binary search has a time complexity of O(log n), where n is the number of elements in the sorted array.

The code implements the insertion sort algorithm, which sorts the array in ascending order. The sorted array will be {1, 2, 3, 4, 5, 6}.