A financial services company is building a fraud detection model using Amazon SageMaker. The ML engineer receives a 40 MB Apache Parquet file as input data. The file contains several correlated columns that are not needed for the model. The engineer needs to drop these unnecessary columns with the least effort while ensuring the data remains compatible with SageMaker for further preprocessing and model training. What should the ML engineer do?