The solution meets the goal because Quantiles normalization with QuantileIndex normalization effectively bins values into quantiles and transforms them into bin indices, which is suitable for feature engineering to predict a target column. The community discussion, with the highest upvoted comment (29 upvotes) and official documentation references, confirms that Quantiles binning mode normalizes values into bins without requiring the target column for the binning process itself. The goal is to create binned features for prediction, not to use the target column in binning, which aligns with Quantiles normalization. Entropy MDL is an alternative that uses the target column, but it's not necessary here. The consensus (67% selecting 'A') and detailed reasoning from authoritative comments support 'A' as correct.