# Model Feature Attribution Drift Violations
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**Note**  
After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Model Monitor, effective 7/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Model Monitor, but we do not plan to introduce new features. For more information, see [Amazon SageMaker Model Monitor availability change](model-monitor-availability-change.md). 

Feature attribution drift jobs evaluate the baseline constraints provided by the [baseline configuration](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModelExplainabilityJobDefinition.html#sagemaker-CreateModelExplainabilityJobDefinition-request-ModelExplainabilityBaselineConfig) against the analysis results of current `MonitoringExecution`. If violations are detected, the job lists them to the *constraint\_violations.json* file in the execution output location, and marks the execution status as [Interpret results](model-monitor-interpreting-results.md).

Here is the schema of the feature attribution drift violations file.
+ `label` – The name of the label, job analysis configuration `label_headers` or a placeholder such as `"label0"`.
+ `metric_name` – The name of the explainability analysis method. Currently only `shap` is supported.
+ `constraint_check_type` – The type of violation monitored. Currently only `feature_attribution_drift_check` is supported.
+ `description` – A descriptive message to explain the violation.

```
{
    "version": "1.0",
    "violations": [{
        "label": "string",
        "metric_name": "string",
        "constraint_check_type": "string",
        "description": "string"
    }]
}
```

For each label in the `explanations` section, the monitoring jobs calculate the [nDCG score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html) of its global SHAP values in the baseline constraints file and in the job analysis results file (*analysis.json*). If the score is less than 0.9, then a violation is logged. The combined global SHAP value is evaluated, so there are no `“feature”` fields in the violation entry. The following output provides an example of several logged violations.

```
{
    "version": "1.0",
    "violations": [{
        "label": "label0",
        "metric_name": "shap",
        "constraint_check_type": "feature_attribution_drift_check",
        "description": "Feature attribution drift 0.7639720923277322 exceeds threshold 0.9"
    }, {
        "label": "label1",
        "metric_name": "shap",
        "constraint_check_type": "feature_attribution_drift_check",
        "description": "Feature attribution drift 0.7323763972092327 exceeds threshold 0.9"
    }]
}
```