The joint distribution function of $(X,Y)$ has no density since $(X,Y)\in D$ almost surely, where $D=\{(x,x^n)\mid x\in[0,1]\}$ has zero Lebesgue measure.
The correlation of $X$ and $Y$ has the sign of $n$ since $Y=u_n(X)$ where the function $u_n:x\mapsto x^n$ is increasing when $n\gt0$ and decreasing when $n\lt0$ (the second case being restricted to $n\gt-1$ since, otherwise $Y$ is not integrable hence the correlation does not exist). The computation of the actual value of the correlation is standard, hence I suggest you signal which specific difficulties, if any, you encounter when performing it.
More generally, one can mention that, for every pair of nondecreasing functions $u$ and $v$ and every random variable $Z$ such that the expectations exist, $\mathbb E(u(Z)v(Z))\geqslant\mathbb E(u(Z))\mathbb E(v(Z))$, hence $\mathrm{Cov}(u(Z),v(Z))\geqslant0$. Obviously, the same conclusion holds if the functions $u$ and $v$ are both nonincreasing, and it is reversed if one function is nondecreasing and the other is nonincreasing.