In which scenario would Quick Sort be less efficient?

Quick Sort can be less efficient in scenarios where the pivot selected for partitioning consistently results in unbalanced subarrays. Specifically, when the pivot is consistently the smallest or largest element in the array, the partitioning process does not divide the data into two approximately equal halves. This leads to the worst-case time complexity of O(n^2), as each recursive call would process an increasingly smaller subarray, effectively turning the algorithm into a linear search.

Choosing a pivot that is either the smallest or largest element in a set where the values are already skewed leads to poor performance, particularly with larger datasets, because it results in numerous recursive calls with minimal work done in each step. In contrast, effective pivot selection that provides balanced partitions is essential for maximizing the efficiency of the Quick Sort algorithm, allowing it to achieve its average-case time complexity of O(n log n).

While factors like the presence of many duplicate values, pre-sorted arrays, or small array sizes can also affect Quick Sort's efficiency, they do not inherently degrade its performance to the same degree as consistently poor pivot selection, particularly in larger datasets.