TheMist AI Efficacy Loopis a key concept within theJuniper Mist AI architecturethat ensures continuous improvement of itsmachine learning (ML)andartificial intelligence (AI)models. This process combines feedback and data collected from multiple sources — including real-world network telemetry, customer success feedback, domain experts, and the Mist data science team — to refine the AI algorithms and enhance prediction accuracy.

According to theJuniper Mist AI Operations and Architecture Guide, the process is described as follows:

“The Mist AI efficacy loop is a closed-loop feedback system that continuously collects data from the field, correlates it with expert knowledge and support insights, and uses it to retrain and improve AI models for greater accuracy and reliability.”

This iterative learning cycle allows Mist AI to:

Improve anomaly detection precision.

Reduce false positives in network issue detection.

Enhance Marvis’s ability to accurately identify root causes and recommend corrective actions.

Theefficacy loopis what differentiates Mist AI from static analytics platforms, as it creates aself-learning systemthat evolves with every deployment and customer interaction.

Therefore, the correct answer isB. Mist AI efficacy loop.