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| Section | Weight | Objectives |
|---|---|---|
| WLAN Configuration | 15% | - Describe Access Category (AC) and QoS concepts - Configure band steering and client load balancing - Describe RRM and Radio Resource Management - Configure rate limiting and traffic shaping - Configure radio settings and RF profiles |
| Advanced Features and API | 0% | - Describe location services and features - Describe webhook and third-party integrations - Describe Mist API structure and capabilities - Describe Bluetooth Low Energy (BLE) and IoT integration |
| Troubleshooting and Analytics | 20% | - Troubleshoot wireless performance issues - Describe client troubleshooting tools - Troubleshoot connectivity issues using Mist portal - Analyze wireless coverage and capacity - Describe event and alarm management - Describe Marvis AI capabilities |
| Access Points and Antennas | 15% | - Describe AP model families and capabilities - Describe antenna types and selection criteria - Describe AP mounting options - Describe AP deployment considerations - Describe AP power options (PoE, DC) |
| Security and Authentication | 15% | - Describe BYOD and device onboarding - Describe RADIUS integration - Configure 802.1X authentication - Describe wireless security concepts (WPA2/WPA3) - Describe captive portal configuration |
| Site Deployment | 20% | - Describe WLAN configuration - Describe SSID and security configuration - Describe the site deployment workflow - Describe Virtual Network (VN) concepts - Perform AP assignment and configuration - Describe AAA server concepts and configuration - Perform site creation and configuration |
| Mist AI and the Wired to Wireless Enterprise | 15% | - Describe the Mist cloud architecture and portal - Describe the components of Mist AI for wireless - Describe the challenges of traditional wireless architectures - Describe the benefits of AI-driven operations |
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NEW QUESTION # 66
What are two ways to access Marvis? (Choose two.)
Answer: C,D
Explanation:
Reference: https://www.mist.com/resources/mist-marvis-overview/
NEW QUESTION # 67
What information would be streamed through webhooks? (Choose two.)
Answer: A,D
Explanation:
According to the Webhooks - Mist document, two types of information that would be streamed through webhooks are:
* Alerts, which are notifications that are triggered by certain events or conditions on the network.
* SLE metrics of clients, which are service level expectations that measure the quality of experience for clients on various aspects such as connection, coverage, capacity, throughput, and roaming.
References: https://www.mist.com/documentation/webhooks/
NEW QUESTION # 68
Site A is configured with a WLAN and a policy. The administrator creates a configuration template at the organization level with additional policies for the same WLAN and mentions Site A in this template.
In this scenario, which statement is correct?
Answer: A
Explanation:
According to the Juniper Mist Wireless Deployment Service Datasheet1, a configuration template at the organization level can be applied to a site group, not to an individual site. Therefore, there is no option to mention the site in the config template at the organization level.
References: https://www.juniper.net/us/en/products/cloud-services/juniper-mist-wireless-deployment-service- datasheet.html
NEW QUESTION # 69
Administrators need to gather information regarding client dwell time and how clients move through a location. In this scenario, which real-time location service provides this capability?
Answer: B
Explanation:
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.
NEW QUESTION # 70
Which real-time location system component takes the coordinates of a tracked device and applies a location- aware triggered action to a client?
Answer: A
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 # 71
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