An ML engineer must run model inferences asynchronously over large datasets and schedule regular checks of model input data quality, with alerts when data-quality changes occur. Which solution satisfies these requirements?

The correct solution leverages Amazon SageMaker batch transform for efficient, asynchronous inference on large datasets. SageMaker Model Monitor is specifically designed to schedule and automate the monitoring of model input data quality, detecting drifts or anomalies, and sending alerts through Amazon CloudWatch Alarms and Amazon SNS. Incorrect options: AWS Glue jobs can run models on a schedule, but CloudWatch alarms alone are not purpose-built for comprehensive data quality monitoring of ML model inputs. AWS Batch jobs can run models, but AWS CloudTrail tracks API activity and resource changes, not data quality within ML pipelines. Amazon ECS with Fargate can host models for inference, but Amazon EventBridge primarily routes events and is not a dedicated data quality monitoring solution for ML models.