What statement is correct about Mist SLEs?

A Mist SLE, or Service Level Expectation, is indeed comprised of multiple classifiers. This design allows the system to assess various dimensions of network performance, such as latency, jitter, and packet loss, across different traffic types and applications. By utilizing multiple classifiers, Mist SLEs can provide a more accurate and comprehensive view of service quality, enabling more effective management and troubleshooting of the network experience for users. This multi-faceted approach is integral to how Mist AI leverages data to optimize network performance and ensure that service levels meet the expectations set for users.

The other statements do not accurately capture the functionality of Mist SLEs. For instance, the notion that a Mist SLE monitors only network bandwidth is too limited and fails to address the broader range of metrics tracked. Additionally, describing a Mist SLE as a single classifier overlooks its complex, multi-classifier framework that enhances its effectiveness. Lastly, while users can interact with settings and configurations in Mist’s system, the core SLE model operates based on algorithms and data analytics rather than manual adjustments by users, emphasizing the automated nature of the SLEs.