JN0-253 Exam Dumps - Mist AI - Associate (JNCIA-MistAI)

The fundamental sampling method used in supervised learning is a labeled dataset. As described in Juniper Mist AI and machine learning application guides, supervised learning algorithms are trained using datasets in which each sample is paired with a known output (label). The model is fed examples comprising input data and the correct answer, learning patterns to map new inputs to their target outputs. While regression, decision tree, and random forest are techniques within supervised learning, the process fundamentally relies on having a labeled dataset for both training and validation phases. “Supervised machine learning models are built on labeled data, with each input paired to a target value or classification.” This allows for accurate model training and evaluation.

TheAI-driven Radio Resource Management (RRM)feature inJuniper Mist Wireless Assurancedynamically optimizesradio resource settingsacross all managed access points (APs) to ensure optimal wireless coverage, capacity, and performance.

According to theJuniper Mist Wireless Assurance and RRM Guide, RRM continuously analyzes live network telemetry, including metrics such assignal strength, interference, channel utilization, and client distribution, to make intelligent adjustments in real time.

It automatically optimizes parameters such as:

Channel selection (to minimize co-channel and adjacent-channel interference).

Transmit power levels (to balance coverage and minimize contention).

Channel width (to optimize throughput based on RF conditions).

The documentation specifies:

“AI-driven RRM in Mist Cloud continuously learns and adapts radio configurations based on environmental and client behavior patterns, improving network stability and overall user experience.”

OptionsA,C, andDare incorrect because:

Band steeringis a specific feature handled separately within WLAN settings.

SSID naming conventionsanduser interfacesare administrative, not RRM-controlled.

Therefore, the correct answer isB. Radio resource settings.

The purpose of Audit Logs in Juniper Mist is to record administrative activities within the Mist dashboard. According to the official Juniper Mist documentation: "Audit Logs provide a detailed chronological record of each configuration change, administrative action, and user activity within the Mist Cloud portal. These logs include information such as the action performed (e.g., adding or removing sites, devices, or users), the timestamp, and the user who performed the action. Audit Logs are critical for accountability, compliance, and troubleshooting, enabling administrators to trace changes and ensure that only authorized users are making modifications to their network settings. Mist stores audit log records securely and makes them accessible for review by organization administrators from the dashboard’s Organization > Audit Logs section."

This directly aligns with option C, as the logs primarily track configuration changes and administrative actions—not performance events, security alerts, or SLE violations. Such activities include site creation, modification, device assignment, role changes, and other user or configuration updates.

The feature that provides deep visibility into BGP operations for MX Series routers is Routing Assurance. According to official Juniper documentation: "Juniper Mist Routing Assurance provides insights about... routing, including routing events, routing protocol states, peer status analysis, routes, and more" for supported routers such as Juniper MX and ACX series. Routing Assurance extends Mist AI observability and analytics into the routing fabric, offering real-time visibility and telemetry for BGP peering session health, routing tables, events, path changes, and other core routing metrics. This empowers network operators with actionable insights and root cause analysis capabilities to maintain optimal WAN and core routing performance from the Mist cloud. Wired Assurance focuses on switch and port health and is not designed for routing protocol visibility.

ThePredictive Analytics and Correlation Engine (PACE)is thecore AI and data analytics componentof the Juniper Mist Cloud architecture. It collects, processes, and correlates telemetry data across all managed domains —Wireless, Wired, and WAN Assurance— to deliver proactive insights and predictive network intelligence.

According to theJuniper Mist AI Operations and Architecture Guide:

“The Predictive Analytics and Correlation Engine (PACE) serves as the foundation of Mist AI, using continuous telemetry ingestion and machine learning algorithms to understand, correlate, and predict network behaviors across the wireless, wired, and WAN environments.”

PACE enables Mist AI to:

Detect anomalies in user experience and device performance.

Predict failures before they affect users.

Correlate events across network domains for root-cause analysis.

Therefore, the correct answer isA. Predictive Analytics and Correlation Engine (PACE).