The "Subsets" pattern is commonly used in coding interviews to generate all possible subsets of a given set of elements. This pattern involves using backtracking or iterative techniques to explore all combinations and build subsets.

Here's an example question that can be solved using the Subsets pattern:

Example Question: Subsets Given a set of distinct integers, return all possible subsets.

Example Input: [1, 2, 3]

Expected Output:

[  [],
  [1],
  [2],
  [3],
  [1, 2],
  [1, 3],
  [2, 3],
  [1, 2, 3]
]

Now, let's go through the solution using both the recursive (backtracking) approach and the iterative approach.

Recursive Solution (Backtracking):

def subsets(nums):
    subsets_list = []
    backtrack(nums, 0, [], subsets_list)
    return subsets_list

def backtrack(nums, index, current_subset, subsets_list):
    subsets_list.append(current_subset[:])  # Add a copy of the current subset to the list

    for i in range(index, len(nums)):
        current_subset.append(nums[i])  # Choose the current number
        backtrack(nums, i + 1, current_subset, subsets_list)  # Explore the next elements
        current_subset.pop()  # Unchoose the current number

# Example usage:
nums = [1, 2, 3]
print(subsets(nums))

Iterative Solution:

def subsets(nums):
    subsets_list = [[]]  # Start with an empty subset

    for num in nums:
        subsets_list += [subset + [num] for subset in subsets_list]

    return subsets_list

# Example usage:
nums = [1, 2, 3]
print(subsets(nums))

Both the recursive and iterative solutions follow the same idea. They generate subsets by iteratively building upon the previously generated subsets. In the recursive solution, we use backtracking to explore all possible combinations. In the iterative solution, we use a loop to iterate over the input numbers and add them to all existing subsets in each iteration.