The correct answer is: False.
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (i.e., uncorrelated with) the preceding components.
PCA is a linear dimensionality reduction technique. This means that it can only be used to reduce the dimensionality of data that is linearly correlated. Non-linear data cannot be reduced in dimensionality using PCA.
In conclusion, PCA is not most useful for non linear type models.