InJuniper Mist AI,Service Level Expectations (SLEs)form the foundation ofWireless AssuranceandWired Assurance. They provide adata-driven, proactive methodto measure and maintain the quality of user experience, going beyond traditional device-centric monitoring.
According to theJuniper Mist AI Operations GuideandWireless Assurance Documentation, SLEs:
“Leverage machine learning and data science to deliver a proactive understanding of end-user experience and identify root causes of performance issues.”
Each SLE (such asTime to Connect,Roaming,Throughput,Coverage, andCapacity) is composed ofclassifiers, which break down performance metrics into measurable, root-cause categories. Examples includeDHCP,DNS,Authentication, andSignal Qualityclassifiers.
This classifier-based structure enables Mist AI to automatically correlate problems and highlight the most probable cause of degradation—eliminating the need for manual troubleshooting or reactive analysis.
OptionsBandDare incorrect because SLEs arenot ad hoc toolsfor manual troubleshooting and donot simply list connected clients. Instead, they provide intelligent, AI-driven insights into user experience across the network.
Therefore, the correct statements are:
A. SLEs use machine learning to provide a proactive approach to understanding the end-user experience.
C. The metrics analyzed to meet specific SLE goals are categorized into classifiers.
[References:– Juniper Mist Wireless Assurance and SLE Overview– Juniper Mist AI Operations and Analytics Guide– Juniper Mist Cloud Monitoring and SLE Classifier Documentation, ]
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