A data scientist builds a Spark ML Pipeline containing a StringIndexer, a VectorAssembler, and a LogisticRegression stage. They call pipeline.fit(trainDF) and store the result in a variable named model. Later, they want to generate predictions on a new, unlabeled DataFrame called newDF. Which method call correctly produces predictions?