Answer: (i) The attributes are correlated

An ordinary decision tree can only make axis-parallel splits of the form $A \le v$ on a single attribute. An oblique tree instead splits a node on a linear combination of several attributes, e.g. $w_1A_1 + w_2A_2 + \dots + w_nA_n \le c$, which produces an oblique (non-axis-parallel) decision boundary in attribute space.

Such a split is useful precisely when the attributes are correlated, because the classes then cannot be separated cleanly by one axis-aligned cut; a linear combination captures the joint effect. If attributes are independent, a simple single-attribute split already works. Two attributes or all-categorical attributes are handled perfectly well by ordinary trees, so (iii) and (iv) are not the reason for using an oblique tree.