You deploy an MLflow model to an Azure Machine Learning batch endpoint to score a folder of 500,000 small CSV files. The initial scoring job runs very slowly, and monitoring shows the compute cluster nodes are underutilized with frequent overhead between individual file reads. You need to improve throughput without changing the scoring logic. Which batch deployment setting should you adjust?