InJuniper Mist AI, variousmachine learning (ML)techniques are applied across the platform to analyze network telemetry and improve performance insights. Among these,unsupervised learningplays a critical role in identifying patterns, trends, and anomalies in unlabeled datasets-data that has not been pre-classified or annotated.
According to theJuniper Mist AI and Machine Learning Overview:
"Unsupervised learning enables Mist AI to automatically detect abnormal patterns or clusters in large volumes of unlabeled network data, allowing the system to identify anomalies and optimize network behavior without human labeling." This method allows Mist AI to autonomously recognize deviations in user experience, traffic behavior, or radio performance without prior definitions, helping to enhance predictive analytics and anomaly detection.
By contrast:
* Supervised learningrequires labeled training data.
* Reinforcement learninglearns through trial-and-error feedback loops.
* Cognitive learningis not a formal ML category-it refers to human-inspired reasoning.
Therefore, the correct answer isD. Unsupervised learning.
References:- Juniper Mist AI and Machine Learning Overview- Juniper Mist Cloud Architecture and AI Engine Guide- Juniper Mist Predictive Analytics and Correlation Engine (PACE) Documentation