K-means clustering is sensitive to the scale of the features because it relies on distance calculations. Features with larger values will have a disproportionate influence on the clustering results. StandardScaler centers the data around zero and scales it to unit variance, which ensures that all features have a similar range and variance. MinMaxScaler scales the features to a range between O and 1, which also addresses the issue of different scales. RobustScaler handles outliers which will then use the other two scaling techniques. Therefore A, B and D are the appropriate scaling techniques. C is not correct as K-means relies on distance calculations and not scaling the data could give some feature a larger weight which isn't the desired outcome. Option E: Using PowerTransformer on 'income' to reduce skewness and StandardScaler on the other features can be a valid approach, but it depends on the distribution of 'income' and the presence of outliers. If 'income' is highly skewed and/or contains outliers, this combination might be more effective than using StandardScaler or MinMaxScaler alone.