The core requirement is to monitor for feature attribution drift in a production environment. The Amazon SageMaker ModelExplainabilityMonitor is the specific tool designed for this purpose. It integrates with SageMaker Clarify and uses explainability algorithms like SHAP (SHapley Additive exPlanations) to calculate feature importance scores for live inference data. By comparing these scores against a pre-computed baseline, it can detect if the model's reliance on certain features has shifted over time, directly addressing the scenario's need to monitor changes in feature prioritization.

A. Analyzing the training dataset with SageMaker Clarify is a pre-deployment step for bias and explainability, not for continuous production monitoring of drift.

B. The ModelQualityMonitor class tracks predictive performance metrics (e.g., accuracy, recall) against ground truth, not the underlying feature importance or attribution.

C. While technically possible, building a custom pipeline is more complex and less efficient than using the purpose-built ModelExplainabilityMonitor provided by SageMaker.

1. Amazon SageMaker Developer Guide: Under the section "Monitor model explainability," it states, "Amazon SageMaker Model Monitor provides the capability to monitor models in production for drift in the attribution of features... The model explainability monitor periodically generates a report that provides insights into which features are most important to your model's predictions."

Source: AWS Documentation, Amazon SageMaker Developer Guide, "Monitor models for data and model quality, bias, and explainability" -> "Monitor model explainability".

2. AWS Machine Learning Blog: In the post "Monitor in-production model explainability with Amazon SageMaker Clarify and Model Monitor," it is detailed that, "The model explainability monitor helps you understand how your model makes predictions in production. It detects feature attribution drift, which is when the relative importance of the features for a model's predictions changes over time."

Source: AWS Machine Learning Blog, "Monitor in-production model explainability with Amazon SageMaker Clarify and Model Monitor," October 20, 2021.

3. Amazon SageMaker Python SDK Documentation: The documentation for the sagemaker.modelmonitor.ModelExplainabilityMonitor class explicitly describes its function as handling "Amazon SageMaker Model Monitor explainability monitoring jobs," confirming it as the correct tool for this task.

Source: AWS Documentation, Amazon SageMaker Python SDK, sagemaker.modelmonitor module, ModelExplainabilityMonitor class definition.