-The second call checks the entire sample data series for anomalies using the [`detect_entire_series`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.operations.anomalydetectorclientoperationsmixin?view=azure-python-preview#azure-ai-anomalydetector-operations-anomalydetectorclientoperationsmixin-detect-entire-series&preserve-view=true) method. This call returns a [`EntireDetectResponse`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.models.entiredetectresponse?view=azure-python-preview&preserve-view=true) that we stored in a variable we named `anomaly_response`. We iterate through the response's `is_anomaly` list, and print the index of any values with a boolean of `true`. Alternatively, we could have used the [`detect_last_point`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.operations.anomalydetectorclientoperationsmixin?view=azure-python-preview#azure-ai-anomalydetector-operations-anomalydetectorclientoperationsmixin-detect-last-point&preserve-view=true) method, which is more appropriate for detecting anomalies in real-time data. To learn more, consult the [best practices guide](../../concepts/anomaly-detection-best-practices.md).
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