  The last success problem is an optimal stopping problem that aims to maximize
the probability of stopping on the last success in a sequence of independent
$n$ Bernoulli trials. In the classical setting where complete information about
the distributions is available, Bruss~\cite{B00} provided an optimal stopping
policy that ensures a winning probability of $1/e$. However, assuming complete
knowledge of the distributions is unrealistic in many practical applications.
This paper investigates a variant of the last success problem where samples
from each distribution are available instead of complete knowledge of them.
When a single sample from each distribution is allowed, we provide a
deterministic policy that guarantees a winning probability of $1/4$. This is
best possible by the upper bound provided by Nuti and Vondr\'{a}k~\cite{NV23}.
Furthermore, for any positive constant $\epsilon$, we show that a constant
number of samples from each distribution is sufficient to guarantee a winning
probability of $1/e-\epsilon$.
