How to easily identify different probability distributions? I am studying currently further option mathematics but i cant identify which distribution to utilize in which question? (these are genearlly word questions?)
We are currently studying geometric,poisson, binomial, negative binomial. I was wondering if someone can explain how to identify which problem uses which distribution?

Ask yourself what thing among what things the random variable counts .

A geometric distribution is that of a count of trials until the first success, in a indefinite sequence of independent Bernoulli trials with identical success rate.   This has a support of $\{1,2,\ldots\}$.
$$X\sim \mathcal{Geo}_1(p)\iff \mathsf P(X=k) = (1-p)^{k-1}p\cdot\mathbf 1_{k\in\{1,2,\ldots\}}$$
Sometime a text might define a geometric distribution as that of a count of failures before the first success, in an indefinite sequence of independent Bernoulli trials with identical success rate.   This has a support of $\{0,1,\ldots\}$.
$$X\sim \mathcal{Geo}_0(p)\iff \mathsf P(X=k) = (1-p)^{k}p\cdot\mathbf 1_{k\in\{0,1,\ldots\}}$$
Note: I like to use the subscript to be clear about which geometric distribution I'm taking about.

A binomial distribution is that of the count of successes among a specified number of independent Bernoulli trials with identical success rate. 
$$X\sim\mathcal{Bin}(n,p) \iff \mathsf P(X=k)~=~\binom n k p^k(1-p)^{n-k}\cdot\mathbf 1_{k\in\{0,\ldots, n\}}$$

A negative binomial distribution is that of the count of successes before a specified number of failures, in an indefinite sequence of independent Bernoulli trials with identical success rate.
$$X\sim\mathcal {NegBin}(r,p)\iff \mathsf P(X=k) = \binom{k+r-1}{k}p^k(1-p)^r\cdot\mathbf 1_{k\in\{0,1,\ldots\}}$$
Also, there are relations between Geometric and certain Negative Binomial distributions, $X\sim\mathcal{NegBin}(1,p)\iff X\sim\mathcal{Geo}_0(1-p)$, and so forth.

A Poisson distribution is that of the count of events within an interval that occur at a constant average rate independent of proximity to other events of the process.
$$X\sim\mathcal{Poiss}(\lambda) \iff \mathsf P(X=k) ~=~\dfrac{\lambda ^k ~e ^{-\lambda}}{k!}\cdot\mathbf 1_{k\in\{0,1,\ldots\}}$$
