What are the possible values of a filter in a CNN?
Weights are learned with backpropogation, that's how model learns to identify different patterns with data. The point of a supervised model is to learn weights.

They can be floating value, they are usually represented as [[101][101][101]], because it identifies vertical line, and it's more intuitive to understand. 

The filter usually do not contain info on depth, they are square matrix with depth equal to number of channels in input layer, with each filter layer spewing one output layer, so to get 4 output layers, you need to have 4 filters

Stride=2 means it will skip 2 every time, that is, it will go from 0,1 to 3,4