Free Juniper JN0-452 Practice Exam with Questions & Answers | Set: 2

In Juniper Networks Mist AI Wireless, controlling client connection quality is a critical part of maintaining high Service Level Expectations (SLEs). One of the most effective ways to ensure good user experience is by enforcing a minimum acceptable RSSI (Received Signal Strength Indication) threshold for client devices. This functionality is implemented using wireless traffic filtering at the WLAN level.

Wireless traffic filtering allows administrators to define minimum and maximum RSSI thresholds that determine whether a client is permitted to associate or remain connected to an SSID. If a client’s RSSI falls below the configured minimum threshold, the AP can deny association requests or deauthenticate the client. This prevents low-signal or “sticky” clients from connecting at poor signal levels, which would otherwise result in excessive retries, low data rates, increased airtime consumption, and degraded performance for all users.

Mist applies these controls intelligently and surfaces their impact through AI-driven analytics. When low-RSSI clients are filtered, Mist can correlate improvements in throughput, latency, and roaming behavior directly to RF health and client quality metrics. This aligns with Mist best practices, which emphasize proactive client quality enforcement rather than reactive troubleshooting.

The incorrect options are explained as follows:

WLAN forwarding determines how traffic is bridged, tunneled, or NATed and has no impact on RSSI enforcement.

Geofencing is used for location-based services and RTLS use cases, not RF signal quality control.

WLAN rate limit restricts bandwidth usage per client or SSID but does not evaluate or enforce signal strength requirements.

Therefore, the correct feature required to set an acceptable minimum RSSI for client devices is wireless traffic filtering, which directly controls association behavior based on signal quality and helps maintain optimal wireless performance.

In Juniper Networks Mist AI Wireless, the behavior described in this scenario is explained by dynamic rate shifting. Dynamic rate shifting is the mechanism by which a wireless client and access point automatically adjust the modulation and coding scheme (MCS) based on current RF conditions to maintain a reliable connection.

When a client is close to an access point, RF conditions are optimal—high RSSI, high signal-to-noise ratio (SNR), and low error rates—allowing the client to use higher-order modulation schemes such as 1024-QAM (in 802.11ax). These modulation schemes deliver higher throughput but require very clean RF conditions. As the client moves farther away from the AP, signal strength decreases and noise becomes more impactful, increasing frame errors and retransmissions.

Dynamic rate shifting continuously evaluates transmission success, retry rates, and acknowledgments. When errors increase, the client steps down to lower modulation rates (for example, from 1024-QAM to 256-QAM or 64-QAM) that are more resilient to reduced signal quality. This trade-off sacrifices peak throughput in favor of connection stability and reliability.

The other options describe contributing factors, but not the mechanism itself:

RSSI indicates signal strength but does not control rate selection.

SNR influences modulation decisions but is not the adaptive process.

Free space path loss explains why signal weakens over distance but does not describe how rates are adjusted.

Mist AI Wireless monitors modulation rates, retries, and throughput via advanced telemetry and uses this data in Wireless Assurance SLEs to highlight RF and performance issues. However, the actual process of stepping up or down transmission rates in response to changing RF conditions is governed by dynamic rate shifting.

Therefore, the correct answer is C. Dynamic rate shifting.

In Juniper Networks Mist AI Wireless, access points discover upstream switch and port information using Link Layer Discovery Protocol (LLDP). LLDP is an IEEE-standard, vendor-neutral Layer 2 discovery protocol designed specifically to allow network devices to advertise and learn information about directly connected neighbors.

When a Juniper Mist AP connects to a wired switch, it uses LLDP to exchange information such as:

Switch hostname and chassis ID

Switch port identifier

VLAN details

Power over Ethernet (PoE) capabilities and power allocation

This information is critical for Mist operations and visibility. It enables the Mist cloud to accurately display AP-to-switch mappings, identify the exact switch port an AP is connected to, validate PoE delivery, and assist with troubleshooting wired-side issues that impact wireless performance. LLDP data is also leveraged by Mist AI and Marvis to correlate wired and wireless events, improving root-cause analysis.

The other options are incorrect for the following reasons:

CDP (Cisco Discovery Protocol) is a Cisco-proprietary protocol and is not used by Juniper Mist APs for switch discovery.

ARP (Address Resolution Protocol) resolves IP addresses to MAC addresses and does not provide device or port identity information.

SNMP is a management and monitoring protocol used by controllers or management systems, not by APs for neighbor discovery.

Because LLDP is standards-based, lightweight, and designed for neighbor discovery, it is the correct and supported protocol used by Juniper Mist APs to learn switch information.

Therefore, the correct answer is D. LLDP.

If a customer wants to broadcast a WLAN at a certain time and day of the week, SSID scheduling should be configured

According to the SSID Scheduling - Mist document1, SSID scheduling is a feature that allows you to customize your WLAN by choosing the exact days and times to broadcast the SSID. This feature can be useful for scenarios where you want to limit the availability of a WLAN to a certain time and day of the week.

In the Juniper Mist architecture, the location system (often referred to as the Mist Location Engine) is the centralized intelligence residing in the cloud that transforms raw RF data into actionable business intelligence. While hardware components like location sensors (Access Points) collect raw signal strength (RSSI) from BLE tags or mobile devices, they do not possess the logic to understand the spatial context or provide data to external applications.

The location system performs the heavy lifting by taking the telemetry gathered from the vBLE antenna arrays on the APs and applying sophisticated machine learning algorithms.1 It calculates the precise X/Y coordinates of a device on a floor plan. However, the true value of the location system—as highlighted in this scenario—is its ability to correlate these coordinates with the venue's metadata, such as defined zones, virtual beacons, and map scales.

Once the location system has determined where a device is relative to the environment, it translates that physical presence into "usable data." This is primarily achieved through the Mist API and Webhooks. For example, when a tracked device enters a specific "Retail Zone," the location system generates an event that an external CRM application can ingest to push a personalized coupon to a customer. Similarly, in healthcare, the location system correlates a nurse's coordinates with a patient room to automatically log a visit in an Electronic Health Record (EHR) system.

By acting as the middleware between the wireless infrastructure and third-party software, the location system ensures that raw coordinates become meaningful data points—such as dwell times, zone entries, or asset movements—that drive automation and enhance user experiences across various industry verticals.

To review and troubleshoot the roaming activity of a specific client within a Juniper Mist environment, administrators rely on the conversational and analytical power of the Marvis Virtual Network Assistant (D) and its underlying Marvis Query Language (C). While other Marvis components like Actions focus on organization-wide health and Minis focus on synthetic testing, the VNA and MQL are specifically designed for deep-dive investigations into individual client experiences.

The Marvis Virtual Network Assistant (VNA) serves as the primary interface for this task. It allows an administrator to interact with the Mist AI using natural language. By typing a request such as "Troubleshoot client [Client Name]" or "How was the roaming for [Client Name]?", Marvis VNA aggregates all relevant telemetry data—including signal strength (RSSI), AP transitions, and authentication timings—into a simplified, human-readable summary. It provides a visual timeline of where the client moved and whether each roam was successful, slow, or failed.

Behind the scenes, these natural language requests are often converted into or powered by the Marvis Query Language (MQL). MQL is a structured way to query the Mist Graph API. For specific roaming reviews, the ROAMINGOF clause is used to filter the massive Mist data set for events related only to that client's mobility. For example, a query like roaming of "User-1" during last 24 hours will return a detailed data set showing the "From AP" and "To AP" details, the delta in signal strength during the handoff, and any associated latency.

By using these two features together, administrators can distinguish between a "sticky client" (one that refuses to roam despite a better signal being available) and a "coverage hole" (where the signal drops too low before a new AP is detected). This AI-driven approach eliminates the need for manual packet captures or log parsing, significantly reducing the Mean Time to Repair (MTTR) for mobility-related issues.

In the Juniper Mist dashboard, managing licenses is handled through the Subscriptions page at the Organization level. This page is critical for ensuring that all deployed hardware (Access Points, Switches, and Gateways) has the necessary cloud services—such as Wi-Fi Assurance, Wired Assurance, or Marvis VNA—to function correctly. The subscription page provides a real-time overview of the organization's licensing health using specific status indicators.

The status Active (D) indicates that the subscription is currently within its valid date range and is being utilized by the hardware in the organization. When a subscription is marked as Active, it means the features associated with that license are fully operational, and the Mist Cloud is actively processing telemetry from the associated devices. This is the primary state for all correctly provisioned licenses.

The status Exceed (B) (often displayed as "Exceeded") is a unique and important state in the Mist ecosystem. Unlike many legacy controllers that might shut down services if a license count is surpassed, Mist uses an "honor-based" system combined with clear visual alerting. The Exceed status appears when the number of physical devices claimed and assigned to a site exceeds the total number of available license seats for a specific service. For example, if an organization has 100 "Wi-Fi Assurance" subscription seats but has deployed 110 Access Points, the status will show as Exceeded.

Mist provides a grace period and persistent notifications to administrators to rectify this by either purchasing more subscriptions or removing excess devices. It is important to note that while terms like "Valid" or "Demo" might be used in general conversation, the specific operational statuses tracked in the subscription table to manage license compliance and expiration are Active, Exceeded, and Expired. These statuses allow IT teams to proactively manage their operational expenditures and ensure uninterrupted service for their wireless users.

In Juniper Networks Mist AI Wireless, Service Level Expectations (SLEs) are used to measure and assure real user experience across the wireless network. Each SLE is built on specific classifiers, which break down failures into root causes such as DHCP, DNS, authentication, RF conditions, or application issues. Understanding which SLEs rely on DHCP classification is essential for accurate troubleshooting.

The two Wireless Assurance SLEs that include a DHCP classifier are Successful Connects and Time to Connect.

The Successful Connects SLE measures whether client devices are able to complete the full connection process successfully. This process includes association, authentication, and critically, DHCP address assignment. If a client fails to obtain an IP address, the connection is considered unsuccessful, and the failure is explicitly classified as a DHCP issue within this SLE.

The Time to Connect SLE measures how long it takes for a client to connect from the moment it initiates association until it is fully usable on the network. One of the key components of this timeline is the DHCP transaction time. Slow DHCP responses, retransmissions, or server delays directly increase the overall connection time, and Mist classifies these delays under DHCP within the Time to Connect SLE.

The other options are incorrect for the following reasons:

Roaming SLE focuses on re-association, authentication, and mobility events, not DHCP, since IP addressing is typically retained during roaming.

Coverage SLE evaluates RF signal strength, SNR, and cell overlap, with no dependency on DHCP.

Throughput SLE measures data transfer performance after connectivity is established and does not analyze DHCP behavior.

Therefore, the correct answer is Successful Connects and Time to Connect, as both SLEs directly depend on and classify DHCP-related performance and failures within Mist Wireless Assurance.

the Post-Install user role is also known as Installer role, which has a minimum level of access to the org, sites, and APs. It can do the initial installation such as claim AP to Org, assign/unassign AP, or place on map, etc., but cannot unclaim or remove the AP from the Org. Site and AP access is allowed based on timed grace period and/or explicit site access.

https://www.mist.com/documentation/mist-ai-mobile-app/