I'm not familiar with this book, but I suspect the idea is to argue with cumulative distribution functions in the following manner: Let $y>0$, then ${\rm F}_Y(y)={\rm P}(Y\leq y)={\rm P}\Bigl(-{1\over\lambda}\ln U\leq y\Bigr)={\rm P}\bigl(U\geq {\rm e}^{-\lambda y}\bigr)$ Now, if the reader thinks it is absolutely essential to turn the last inequality sign the right way before computing the probability, it is possible to use the hint and say that the last expression is $={\rm P}\bigl(1-U\geq {\rm e}^{-\lambda y}\bigr)={\rm P}(U\leq1-{\rm e}^{-\lambda y}\bigr)={\rm F}_U\bigl(1-{\rm e}^{-\lambda y}\bigr)=1-{\rm e}^{-\lambda y}$ This is the desired cumulative distribution function, so $Y$ is exponentially distributed with parameter $\lambda$, as stated.
Of course, we would say that it is much more natural to use ${\rm P}\bigl(U\geq {\rm e}^{-\lambda y}\bigr)=\int_{{\rm e}^{-\lambda y}}^1{\rm d}u=1-{\rm e}^{-\lambda y}$ but for some reason students often seem to disagree with us on that...