Monitoring and logging are indispensable in ML pipeline automation, especially in production environments with high demands. They offer real-time insights for performance optimization (A) and ensure operational reliability by enabling the early detection and resolution of issues (C). While they introduce additional components to the pipeline, their benefits in terms of efficiency and reliability far outweigh any perceived complexity. They do not, however, eliminate the need for data preprocessing (D), as preprocessing is a separate step necessary for preparing data for ML models. The correct options are A and C, as they collectively cover the essential reasons for implementing monitoring and logging in ML pipelines.