The correct answer is False.
The likelihood function is a probability density function that measures the probability of observing a particular set of data given a set of parameters. The log likelihood function is the logarithm of the likelihood function. The log likelihood function is always non-negative, and it is equal to zero when the data is perfectly explained by the model.
The maximum likelihood estimator is the value of the parameters that maximizes the likelihood function. The minimum log likelihood estimator is the value of the parameters that minimizes the log likelihood function.
It is not always true that minimizing the likelihood is the same as maximizing -2 log likelihood. For example, if the likelihood function is a quadratic function, then the minimum of the likelihood function is also the maximum of the log likelihood function. However, if the likelihood function is a more complicated function, then the minimum of the likelihood function may not be the maximum of the log likelihood function.
In general, it is not possible to say whether minimizing the likelihood is the same as maximizing -2 log likelihood without knowing more about the specific likelihood function.