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You are a machine learning engineer responsible for maintaining an ML pipeline that processes customer transaction data to detect fraudulent activity. The pipeline includes several stages: data ingestion, data preprocessing, model inference, and result storage. The pipeline runs continuously, processing large volumes of data in near real-time. Recently, you’ve noticed that some transactions are being incorrectly classified as non-fraudulent, raising concerns about potential anomalies or errors in the data processing or model inference stages. Given the critical nature of the pipeline, which approach is the MOST EFFECTIVE for addressing these issues?