How to find the score function of $(\theta_1,\theta_2)$

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Given a Weibull model:

$\Pi_{i=1}^{n} \lambda \kappa (\lambda y_i)^{\kappa -1} \exp\{-(\lambda y_i ) ^\kappa \}$

How to find the score function of $(\kappa,\lambda)$

Does this mean we take the derivative of the log likelihood with respect to $\kappa$ then take the derivative of that result with respect to $\lambda$?

Or do we create a 2x1 vector where the top is derivative of the log likelihood with respect to $\kappa$ and the bottom is the derivative with respect to $\lambda$