I have a pet peeve about Monte Carlo methods; although it might be more fairly characterised as a rookie mistake I saw once. MCM are not strong if the tail variance isn't an important feature of what is being modeled. I've seen simulations where the modeler starts with an analytic model - from which they could trivially calculate the mean and variance of a KPI - then used a MCM simulation to find out essentially what…
For something as straightforward as Bernoulli, where you plug values into a formula and are done, there is no reason to use MCM. However, I am in agreement that in more complicated cases (e.g. a probability distribution is based on some result of one Markov or Markov-like calculation — or possibly multiple), and the theory is slightly or much more complicated then there is a lot of value in Monte Carlo methods.