This is a short answer question. Give points/results.

Given a data matrix D with d = 5 columns with a total variance of 100, an analyst performs a PCA via eigenvalue decomposition, with the resulting eigenvalues as [35,25,20,15,5]. If the analyst wishes to reduce dimensionality with 80 % of variance explained, how many dimensions would the analyst be able to reduce down to? What would be the standard deviations of the data for these selected dimensions