What are the methods to ensure that the population split for A/B test is random?
Just split them 50/50 (or another percentage, depending on what you want). Once you want to test a specific metric, make sure that if you can only generate that for a portion of the A group because of some specific property (ie you need information C for this metric) to compare it only to the people in group B that also have information C to avoid bias.

Example: I have 200,000 customers. I split them 50/50. I'm testing my new recommendation method against a random advertisement. However my recommender only works if they have searched in the last month. After getting back the results, I will have to check that against the control group filtered to have only people that have searched in the past month. People that have searched in the past month are much more likely to have a stronger kind of connection to you, so if you would check against the whole group you would overestimate the performance of your recommender.

Alternatively you could already split up the groups knowing whether they have information C or not, but you might want to test different things so splitting before hand can give you extra info, at the small cost that your groups might be slightly unequal, but for a big sample size this is not an issue at all.