Firstly, it depends what kind of random number it is. If you flip two coins(0/1) and take the average (technically in [0,1]), you'll get a very different kind of random number than if you say every number [0,1) is equally likely to be chosen.
Secondly, looking at the three you mention, if you're interested in [0,1) exponential or gaussian would be bad models as they take values in $[0, \infty)$ and $(-\infty, \infty)$ respectively. Uniform(0,1) would be the natural choice, but as above, other brands are available.
The mass of the function in [0.5, 0.6] will entirely depend what distribution is used. If Uniform(0,1) then 0.1.
When you prescribe "random number" by definition it is the uniform distribution
... not at all. There are infinitely many distributions with domain of support on (0,1). A random variable whose distribution has not been specified is just a random variable whose distribution has either not been specified, or is not known. $\endgroup$