Are there hypothesis classes that are hard to learn but easy to test?
Take the class  $\mathcal{M}$ of monotone boolean functions under the uniform distribution on $\{0,1\}^n$: 


it is known that $O(\sqrt{n}/\varepsilon^2)$ queries are sufficient to test it (even with non-adaptive testers) [KhotMinzerSafra15].
learning $\mathcal{M}$ under the uniform distribution, even allowing membership queries, requires $2^{\Omega(\sqrt{n}/\varepsilon)}$ queries ([BshoutyTamon96,BlaisCOST15]).