If it says that $X$ and $Y$ are chosen "independently" over the set $\{1, ..., 13\}$ then it means $X$ and $Y$ are independent random variables:
$$ P[X=i,Y=j]=P[X=i]P[Y=j] \quad \forall i, j \in \{1, ..., 13\} \quad (Eq. 1)$$
If $X, Y$ are both uniform over that set then
$$ P[X=i]=P[Y=i]=1/13 \quad \forall i \in \{1, ..., 13\} \quad (Eq. 2)$$
So it is possible to have $X=Y$. The equations (1)-(2) are consistent with the numbers $X,Y$ being chosen in a sampling with replacement experiment where all $13^2$ combinations are equally likely.
It would be impossible to get equations (1)-(2) in a sampling without replacement experiment. In particular, if we first pick $X$, and we next pick $Y$ from a set that depends on $X$ to ensure $Y\neq X$, then it means $Y$ is dependent on $X$. Indeed
$$P[Y=1]=1/13, P[Y=1|X=1]=0$$
The sampling without replacement scenario can be described without the concept of independence: We first pick $X$ uniform over $\{1, ..., 13\}$. Then for each $i \in \{1, ..., 13\}$, given that $X=i$, we choose $Y$ with a conditional distribution that is uniform over the set $\{1, ..., 13\} - \{i\}$. (Or, more simply, choose two distinct numbers over the set $\{1, ..., 13\}$, with all possibilities equally likely). I agree with JMoravitz that it is also a good scenario to solve for, but that situation does not seem (to me) to have "independence" anywhere (as Saulspatz also notes). In particular, for that situation, I cannot find two events that are independent of each other.