Amazon SageMaker Model Monitor is the correct option because it provides continuous monitoring of deployed models for prediction accuracy fairness and data drift and it supports scheduled evaluations with alerting.

Amazon SageMaker Model Monitor is purpose built to detect data quality and model quality issues by comparing production traffic to baselines and it can run configurable monitoring jobs on endpoints and emit metrics for alerting so teams can surface problems quickly.

Amazon SageMaker Clarify helps with bias analysis and explainability during training and evaluation but it does not provide continuous scheduled monitoring on live endpoints in the same operational way as the Model Monitor.

Amazon SageMaker Model Cards provides governance and documentation about model attributes and intended usage and it is focused on transparency rather than running ongoing operational checks on live predictions.

Amazon CloudWatch collects infrastructure and application telemetry and can host alarms and dashboards but it does not include native ML workflows for data drift bias or automated model quality monitoring that compare live traffic to baselines.

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