I am having a tough time remembering the properties as well as the mean and variance of weibul, gamma, GPD, poisson etc.
Do we have to go through these.
These are unavoidable-s 
I have to disagree - I would say knowing the mean and variance of those distributiions will not help you on the exam. The questions just weren’t of that nature in my opinion (at least for the exam that I took). I would say that the Gamma and Weibul distributions are way in the weeds and there are other things in the material that you should concentrate on before these.
What I would say though is that you may very well get a question where you need to calculate the predicted number of occurances according to the Poisson distribution, given some inputs.
So you would do well to remember the formula for the Poisson distribution and know how to actually use it to answer calculation questions.
I think S666 is correct…I was keeping in view that thr is some syllabus change…but there is not a much change from the exam which we had given.
Part 1 only covers following:
Distinguish the key properties among the following distributions: uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, normal distribution, lognormal distribution, Chi-squared distribution, Student’s t, and F-distributions, and identify common occurrences of each distribution. • Describe the central limit theorem and the implications it has when combining i.i.d. random variables. • Describe independent and identically distributed (i.i.d) random variables and the implications of the i.i.d. assumption when combining random variables. • Describe a mixture distribution and explain the creation and characteristics of mixture distributions.Distinguish the key properties among the following distributions: uniform distribution, Bernoulli distribution, Binomial distribution, Poisson distribution, normal distribution, lognormal distribution, Chi-squared distribution, Student’s t, and F-distributions, and identify common occurrences of each distribution. • Describe the central limit theorem and the implications it has when combining i.i.d. random variables. • Describe independent and identically distributed (i.i.d) random variables and the implications of the i.i.d. assumption when combining random variables. • Describe a mixture distribution and explain the creation and characteristics of mixture distributions. And Stats part cover following for Part 1 may 2016 exam: Interpret and apply the mean, standard deviation, and variance of a random variable. • Calculate the mean, standard deviation, and variance of a discrete random variable. • Interpret and calculate the expected value of a discrete random variable. • Calculate and interpret the covariance and correlation between two random variables. • Calculate the mean and variance of sums of variables. • Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. • Interpret the skewness and kurtosis of a statistical distribution, and interpret the concepts of coskewness and cokurtosis. • Describe and interpret the best linear unbiased estimator.
Just to edit my previous post - I would suggest actually that you SHOULD know the mean and variance of a Poisson distribution - and in fact most of the other distributions listed in anandmishra’s post. Of course the Poisson mean and variance is just Lambda so that is easy to remember.
Knowing the mean and variances etc for the more common distributions (uniform distribution, Bernoulli distribution, binomial distribution, Poisson distribution, normal distribution) could in fact be very helpful on the exam - my comment was more aimed at the “left field” distributions like Weibul and Gamma. Those two are not part of the core learning requirements in my opinion.