According to the Mist documentation 1 , asset tracking is a service that allows you to find key assets and people with detailed analytics based on their location using Mist-supported third-party BLE beacons. To enable asset tracking, you need to configure both the BLE beacons and the Mist cloud settings.

The Show assets check box is a setting in Live View that allows you to display or hide the assets on the floor plan 2 . If this check box is not selected, you will not be able to see the third-party beacons on Live View.

The Locate this Client as an Asset check box is a setting at the BLE client level that allows you to treat a BLE client as an asset for location purposes 3 . If this check box is not selected, you will not be able to track the BLE client as an asset.

According to the Marvis - Mist document, Correlation is a category in Marvis’ troubleshooting response that shows when the AP went offline. Correlation shows the correlation between events and SLEs for a given client or AP.

According to the exhibit, the organization has a total of two licenses for Marvis wireless, and one of them is in use. Therefore, the organization has one available license and one used license for Marvis wireless.

According to the exhibit, the WLAN has a minimum basic rate of 24 Mbps for both 2.4 GHz and 5 GHz bands. This corresponds to the high-density data rate setting in Mist, which disables all data rates below 24 Mbps. This setting is recommended for high density designs, where clients have enough signal strength and quality to operate at higher rates.

According to the Cisco Catalyst 9800 Series Wireless Controller Software Configuration Guide 3 , three types of devices that are counted by default for occupancy analytics are:

Mobile app clients, which are devices that have a mobile app installed with the Mist SDK or BLE beaconing enabled.

Cellular clients, which are devices that have cellular connectivity and can be detected by Mist APs using LTE sensing.

Wi-Fi clients, which are devices that have Wi-Fi connectivity and can be detected by Mist APs using Wi-Fi scanning.

In the Juniper Mist AI ecosystem, Zone Analytics is the specific real-time location service component designed to provide deep business intelligence regarding how users and devices interact with a physical space. While the Mist platform offers various location services—such as Wayfinding for turn-by-turn navigation and Asset Tracking for locating specific tagged equipment—Zone Analytics focuses on the aggregate behavior of clients within defined boundaries on a floor plan.

Zone Analytics allows administrators to define virtual areas, known as zones, on their facility maps. The Mist Location Engine then uses its patented virtual Bluetooth Low Energy (vBLE) antenna array and machine learning algorithms to track the presence of mobile devices (via the Mist SDK) and Wi-Fi clients within these zones. This service calculates two primary metrics required by the scenario:

Dwell Time: This measures the duration a specific client remains within a defined zone. Administrators can set minimum and maximum dwell thresholds to categorize visitors (e.g., " passersby " vs. " engaged customers " ).

Movement Patterns: By analyzing transitions between different zones, the service generates " motion flows " or " visitor journey " maps. This helps organizations identify common paths taken through a building, popular entry/exit points, and areas of high congestion.

These insights are typically presented through the Engagement Analytics or Premium Analytics dashboards. For example, a retailer might use Zone Analytics to determine if a new product display is successfully increasing the " dwell time " of shoppers in a specific aisle, or an office manager might use it to optimize " workflow " by seeing how employees move between different departments. By leveraging these real-time data sets, businesses can make informed decisions about staffing, floor layouts, and resource allocation based on actual user behavior patterns.

In Juniper Networks Mist AI Wireless Real-Time Location System (RTLS) design discussions, multiple location technologies are commonly compared based on accuracy, reliability, and use-case suitability. Among the listed options, Ultra Wide-Band (UWB) provides the greatest location accuracy .

UWB achieves superior accuracy because it uses very short-duration radio pulses across a wide frequency spectrum , enabling extremely precise time-of-flight (ToF) and time-difference-of-arrival (TDoA) measurements. This allows UWB systems to determine device location with decimeter-level precision , often in the range of 10–30 centimeters , even in complex indoor environments. UWB is also highly resistant to multipath interference, which is a major limitation of Wi-Fi and BLE-based positioning systems.

By comparison:

Wi-Fi–based location relies on RSSI and sometimes Angle of Arrival (AoA), typically achieving 3–5 meter accuracy under good conditions.

BLE badges improve accuracy over Wi-Fi by using beacon proximity and BLE signal characteristics, often achieving 1–3 meter accuracy , depending on beacon density and calibration.

SDK-enabled mobile applications can enhance BLE or Wi-Fi accuracy by leveraging motion sensors and device context, but they are still constrained by the underlying RF technology and rarely reach sub-meter precision.

While Juniper Mist’s vBLE and AI-driven probability surfaces deliver industry-leading accuracy for Wi-Fi and BLE-based RTLS use cases such as wayfinding, asset visibility, and proximity engagement, UWB remains the most accurate indoor positioning technology overall . Its precision makes it ideal for high-value use cases such as medical equipment tracking, industrial automation, and secure access control.

Therefore, the correct answer is Ultra wide-band (UWB) , as it provides the highest level of location accuracy among the listed solutions.

In Juniper Networks Mist AI Wireless , Radio Resource Management (RRM) uses multiple decision-making systems to adapt the RF environment intelligently. A channel change triggered by the detection of RADAR on a DFS (Dynamic Frequency Selection) channel is a clear example of an event-driven RRM action .

DFS channels are regulated frequencies that must be shared with weather, military, and aviation radar systems. Regulatory bodies require wireless devices to immediately vacate a DFS channel when radar activity is detected. When a Juniper Mist AP operating on a DFS channel detects radar signatures, it generates a real-time RF event . This event forces the AP to immediately stop transmitting on that channel and move to a new, non-DFS or alternate DFS channel.

Mist classifies this behavior as event-driven RRM because:

The channel change is reactive , not predictive

It is triggered by a specific external RF event (radar detection)

It requires immediate action to maintain regulatory compliance and avoid interference

The other options are incorrect for the following reasons:

FCC compliance defines why the change is required, but it is not an RRM system or decision model.

Global optimization refers to long-term RF tuning across the network based on historical performance data, not immediate forced changes.

Reinforcement learning is used by Mist to optimize RF parameters over time through continuous learning, not for mandatory, instantaneous responses to radar events.

Mist RRM combines event-driven actions , AI-based global optimization , and reinforcement learning to deliver both compliance and performance. In the case of radar detection on a DFS channel, the AP’s channel change is driven by a real-time RF event , making event-driven RRM the correct answer.