Consider $N$ independent random variables $X_{1}, X_{2}, \ldots, X_{N}$ such that \begin{equation} X_{i} \sim \Gamma\left(1, \frac{1}{N}\right), \end{equation} for $i \in [N]$. Let \begin{equation} Y = \sum_{i=1}^{N} X_{i}. \end{equation}
I am trying to find the joint PDF $$f_{\left(X_1, X_2, \ldots, X_{N-1}\right)|Y=1}(x_{1}, x_2, \ldots x_{N-1}).$$
Can we conclude from our given information that the joint PDF is the PDF of a $\text{Dirichlet}(1, 1, \ldots, 1)$ distribution?
This answer tackles a similar question, but I am not sure how the answer concludes that the Beta marginals imply a joint distribution which is a Dirichlet (as a comment states, the marginals contain insufficient information about the joint). In other words, my confusion with the question I linked to is this:
Is it true that, for this case at least, the distribution for the random variable $\frac{X_i}{X_1+\cdots+X_{N}}$ is the same as the distribution for the random variable $X_{i} | X_1+\cdots+X_{N} = 1$?
If so, just out of curiosity, how general is this result? Does it hold for any distribution and any random variable?