Decision Tree
The Decision Tree inductive learning algorithm may be used to generate "IF ... THEN" rules that are consistent with a set of given exs. X1, X2, , X10 are used to classify a binary output variable (Y).

Please show detailed process how you obtain the solutions.

At most how many leaf nodes can a decision tree have if it is consistent with a training set containing 100 exs?

So far I have:
With the ten Boolean attributes for n, it gives us or 21024 different functions to choose from, that is a huge number. Since that would mean as many possible leafs since a leaf specifies the value to be returned by a function.

In this scenario we have no way of observing an attribute that will distinguish the exs. Therefore, split the total number of exs evenly into positive and negative exs since no matter what attribute you choose, they are all binary.
I = p + n, I = 100, p = 50, n = 50