Here's what happens. The probability that Y takes on any particular value is actually zero, because the probability that X takes on any particular value is zero. What we can talk about is the probability that Y takes on a value in an interval.
So, for example, what is the probability that Y is on [0,1] in your problem? Clearly it's 1 because no matter what X is, Y will be on that interval. Now suppose we look at [itex]x=\frac{1}{2}[/itex]. If [itex]x=\frac{1}{2}[/itex], then what is Y? Well, we can use the Pythagorean Theorem, and see that
[tex]y=\sqrt{1^2 - \left(\frac{1}{2}\right)^2} = \frac{\sqrt{3}}{2}[/tex].
Now it's pretty easy to see that if x is greater than [itex]\frac{1}{2}[/itex], then y is smaller than this, so we know that the probability that [itex]Y < \frac{\sqrt{3}}{2}[/itex] is the same as the probability that [itex]x > \frac{1}{2}[/itex], which is [itex]\frac{1}{2}[/itex]. More generally, if we want to know the likelihood that Y is less than some particular value [itex]y_0[/itex], we have that this happens as long as
[tex]x > \sqrt{1-y_0^2} [/itex], which happens with probability 1 - that. So we have the following cumulative distribution function:<br />
<br />
[tex]P[Y < y] = 1 - \sqrt{1 - y^2}[/tex]<br />
<br />
And the density function is just the derivative of that, which is:<br />
<br />
[tex]f(y) = \frac{y}{\sqrt{1 - y^2}}[/tex][/tex]