Best method to deal with too many zeroes in regression problem?
Option (b) is a good approach. If you use a classifier that outputs probabilities, you can even multiply the two model outputs together to calculate a risk expectation that's interpretable as the joint likelihood.

If cpc is discrete valued and non negative, you could interpret that as a count and use a zero-inflated Poisson model. If it's not, you could convert it by rounding to the nearest cent or something like that.