Which searching technique has a best-case time complexity of O(1)?

The correct answer is based on the characteristics of how a hash table operates. In a hash table, data is structured in a way that allows for very efficient key-based access. When searching for an element using its key, the hash function computes an index that corresponds directly to the location of the data in the hash table.

In the best-case scenario, this process allows the search to be completed in constant time, O(1), because the hash function can uniquely map the key to a specific index without needing to traverse any other data elements. This efficient access is one of the main advantages of hash tables compared to linear and binary searches.

Linear search, for instance, would need to check each element one by one, resulting in a best-case time of O(1) only if the element is the first one checked. Binary search, on the other hand, requires a sorted array and reduces the search space logarithmically, but still is O(log n) in the best case. Jump search takes a specific stride through data but is also not constant time in the best case.

Thus, when looking at the time complexities involved in these searching techniques, the hash table’s design allows it to achieve that optimal O(1) complexity in the