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Your questions is unclear but I guess you mean that for the return of stock A you find a model $$ r_A = (0.5, 0.75) (r_F^1, r_F^2) + \epsilon_A $$ where $r_F^i$ are the factor returns and $\epsilon_A $ is an uncorrelated error. Let us denote $e_A = (0.5, 0.75)$, the exposure of stock $A$ to the factors. For $B$ you have $$ r_B = (0.75, 0.5) (r_F^1, r_F^2) ...
The factors are the same for both stocks, so there is just one factor covariance matrix for both A and B. Factor models are a way to reduce the dimension of a problem. If every stock had its own set of factors, this would increase the problem dimension.
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