Yes, exactly! I explained it at the 4th point of the "Guidelines to choose the best number of principal components" section. Creating the cumulative explained variance plot is the best way to choose the right number of components for PCA. Let's say, for example, we use PCA(n_components=0.9) and the algorithm chooses 10 compoents that describe 90% variance in the data. After creating the cumulative explained variance plot, we also identified that 88% of the variance can be explained by only 7 components. So, we can go with 7 components. We only lost 2% variance, but now there are only 7 components. This is just an example that shows the importance of creating the cumulative explained variance plot.