Bagging and random forest are:

A) similar, but bagging uses datasets resampled in cross-validation fashion (without replacement), while random forest uses bootstrapped datasets

B) very simial. Random forest allows considering a random subset of m predictor variables at each split, where m is any number (parameter of the random forest model), while in bagging the m itself is randomly chosen from the range 1...p at each split (where p is the total number of predictor variables)

C) essentially the same procedure, computationally. Bagging can be thought of as a random forest where all p independent variables are considered for each split (i.e. m=p)

D) two very different approaches with little in common