A data scientist is building a Spark ML Pipeline to predict customer churn. The raw DataFrame contains a categorical column 'region' (string) and several numeric columns. They want to one-hot encode 'region', assemble all features into a single vector, and train a LogisticRegression model. Which ordering of Pipeline stages will produce a valid, working pipeline?