Why is it preferable to use a linear search when searching smaller datasets?(1 point)%0D%0AResponses%0D%0A%0D%0AA linear search is more efficient with its memory allocation when searching smaller datasets.%0D%0AA linear search is more efficient with its memory allocation when searching smaller datasets.%0D%0A%0D%0AA linear search is easy to implement and faster than other algorithms when searching smaller datasets.%0D%0AA linear search is easy to implement and faster than other algorithms when searching smaller datasets.%0D%0A%0D%0AA linear search is the best option for smaller datasets and larger datasets, too.%0D%0AA linear search is the best option for smaller datasets and larger datasets, too.%0D%0A%0D%0AA linear search requires fewer lines of code to search smaller datasets.
A linear search is preferable when searching smaller datasets because it is easy to implement and faster than other algorithms. It requires fewer lines of code and does not require additional memory allocation.
The correct response is:
A linear search is easy to implement and faster than other algorithms when searching smaller datasets.
When searching smaller datasets, it is preferable to use a linear search because it is easy to implement and faster than other algorithms. A linear search involves checking each element of the dataset one by one until a match is found. This approach is efficient for smaller datasets because the time complexity of a linear search is O(n), where n is the number of elements in the dataset. In other words, the time it takes to perform a linear search increases linearly with the size of the dataset.
Compared to other search algorithms like binary search or hash tables, which have faster average or best-case scenarios, a linear search is more suitable for smaller datasets because it requires fewer lines of code and doesn't need any extra memory allocation.
For larger datasets, other search algorithms that have better average or best-case time complexity may be more efficient. However, for smaller datasets, a linear search provides a simple and efficient solution.