A hospital is using an ML model to validate x-ray results.

Option A is correct because Amazon SageMaker Model Monitor is the AWS service specifically built to monitor data quality and model quality for ML models in production. AWS documentation states that Model Monitor supports continuous monitoring with a batch transform job that runs regularly and also supports on-schedule monitoring for asynchronous batch transform jobs. That aligns directly with the scenario of a hospital running a nightly batch inference job and needing a daily report on both the incoming data and the model’s predictive performance.

AWS documentation also separates the two monitoring needs very clearly. Data quality monitoring can be scheduled for batch transform jobs by using DefaultModelMonitor with a BatchTransformInput. Model quality monitoring can also be scheduled for batch transform jobs by using ModelQualityMonitor, which compares predictions against actual ground-truth labels stored in Amazon S3. Since the question explicitly asks for both model data quality and model performance, SageMaker Model Monitor is the documented feature that covers both requirements together.

Option B is not sufficient because CloudWatch dashboards show operational and resource metrics, not the full ML-specific data quality and model quality reports required here. Option C is incorrect because AWS Glue DataBrew is for data preparation and profiling, not model performance monitoring. Option D is partially plausible because SageMaker Pipelines integrates with QualityCheck steps and can run monitoring jobs on demand, but the AWS docs position Model Monitor as the native solution for scheduled monitoring of production batch inference workloads. Therefore, the best AWS-documented answer is A.