You operate a deployed Azure Machine Learning online endpoint that has inference data collection enabled. Stakeholders report that upstream data pipeline changes occasionally send records with missing values and out-of-range numeric fields, which degrade predictions before any drift is visible. You need to configure an Azure Machine Learning model monitor that specifically catches these problems as they occur, using thresholds on null rates and value ranges. Which monitoring signal should you configure?