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Juniper Mist AI Wireless, Specialist (JNCIS-MistAI-Wireless) Sample Questions (Q78-Q83):
NEW QUESTION # 78
A college is experiencing poor Wi-Fi performance in a lecture hall. An administrator reviews the SLE dashboards and finds that while Coverage and Successful Connects are good, the Capacity and Throughput SLEs are very low during class times. The primary classifier for the Capacity failure is "High Bandwidth Utilization," and the primary classifier for the Throughput failure is
"Capacity." What is the most logical conclusion and the best course of action?
Answer: D
NEW QUESTION # 79
What do 802.11 stations perform to help avoid collisions on the WLAN medium?
Answer: B
Explanation:
Reference: https://blogs.arubanetworks.com/industries/understanding-802-11-medium-contention/
NEW QUESTION # 80
In which two layers of the OSI model are WLANs located? (Choose two.)
Answer: A,C
Explanation:
Reference: https://www.controleng.com/articles/wi-fi-and-the-osi-model/
NEW QUESTION # 81
An administrator is deploying a new wireless network and wants to use the autoplacement feature to position the APs on the floorplan.
Which two conditions must be met before running the autoplacement process for a new site?
(Select all that apply.)
Answer: A,E
NEW QUESTION # 82
Which real-time location system component takes the coordinates of a tracked device and applies a location- aware triggered action to a client?
Answer: D
Explanation:
In the architecture of a Mist AI Real-Time Location System (RTLS), the process of moving from raw signal data to an actionable event involves several distinct layers. To understand why the location system is the correct answer, we must examine how Mist processes Location-Based Services (LBS).
The workflow begins with location sensors (typically Mist Access Points). these sensors collect raw Bluetooth Low Energy (BLE) or Wi-Fi signals (RSS measurements) from tracked assets or mobile devices.
These raw data points are then transmitted to the cloud-based Mist AI engine. The location system (often referred to as the Location Engine within the Mist Cloud) is the specific functional component responsible for calculating the precise X/Y coordinates of the device using machine learning and trilateration algorithms.
However, its role does not end at mere coordinate calculation. The Mist location system is designed to be
"context-aware." Once the coordinates are established, the system maps these coordinates against defined zones, virtual beacons (vBeacons), or geofences configured in the Mist dashboard. When a device's coordinates intersect with a predefined boundary, the location system triggers a location-aware action. This could include sending a webhook to a third-party application, pushing a proximity notification to a mobile SDK client, or recording a "visit" in the analytics engine.
While Juniper Mist Edge handles data plane tunneling and Marvis Minis acts as a digital twin for network simulation, they do not manage the coordinate-to-action logic. The location system is the intelligence that bridges the gap between spatial data and business logic, ensuring that the right client receives the right trigger based on their physical presence within a venue.
NEW QUESTION # 83
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