Concept:
Monte Carlo simulation is a computational technique that uses random sampling to solve problems that are difficult to solve analytically.
Step 1: Understand Monte Carlo simulation.
Monte Carlo methods are useful when the system contains randomness or uncertainty.
Step 2: Identify suitable systems.
If a system is probabilistic, complex, and difficult to solve exactly, Monte Carlo simulation is useful.
Step 3: Eliminate incorrect options.
If a deterministic exact solution exists, Monte Carlo may not be necessary.
If no random variables are involved, Monte Carlo is not suitable.
If the system is simple and linear, direct mathematical methods may be enough.
Therefore:
\[
\text{Monte Carlo simulation is useful when the system is probabilistic and complex.}
\]
\[
\therefore \text{Correct Answer is (B)}
\]