A dataset has two mean-centred features with covariance matrix
You run PCA and keep only the first principal component, projecting the data onto a single direction.
What fraction of the total variance survives, and what is the unit vector along that component?
Once you get it right, the reasoning behind the answer shows up here.
Select all that apply.
Rotate the axis until it lines up with the data. Find the angle where the spread is easiest to see.
The information never changed. Only the viewpoint changed. PCA searches for the best viewpoint automatically.