Free Juniper JN0-253 Practice Exam with Questions & Answers | Set: 3

Marvis Minisis a feature withinMarvis Virtual Network Assistant (VNA)that proactively tests and validates network performance by simulating user connections. Instead of waiting for user complaints, it performssynthetic client transactionsto confirm the functionality of critical network services.

According to theJuniper Mist Marvis Minis Technical OverviewandMarvis AI Operations Guide, Minis validates several network functions automatically, including:

Dynamic Host Configuration Protocol (DHCP):Ensures clients can obtain IP addresses properly from DHCP servers, validating end-to-end DHCP exchange success and response time.

Address Resolution Protocol (ARP):Verifies correct IP-to-MAC address resolution, confirming that clients can reach devices on the same subnet and that ARP tables are updated correctly.

Marvis Minis also performs checks onDNS resolution, authentication, and application reachability, but not on SNMP or NTP. Those protocols are used for management and time synchronization, not end-user connectivity validation.

Thus, the correct answers areB (DHCP)andD (ARP).

To create a real-time visualization dashboard that displays clients on a map, use Live View and WebSocket APIs. The "Live View" feature in Juniper Mist provides a graphical, interactive map view of clients (devices) and assets as they move through a site, with location data updated in real time. The Juniper Mist WebSocket API enables developers and integrators to subscribe to client location topics and stream position data directly to custom dashboards. According to the documentation: "WebSockets open a bidirectional communication session for sending and receiving real-time event-driven responses... Useful in circumstances where you want to avoid browser refreshes and display real-time location data." Combining these allows you to build a dashboard showing live client positioning, as seen in sample dashboards and integration guides for Mist.

InJuniper Mist Wireless Assurance, theCoverage SLE (Service Level Expectation)measures the quality and consistency of Wi-Fi signal coverage across an organization’s wireless infrastructure. It provides insight into how well clients can connect to access points with sufficient signal strength and quality.

According to theJuniper Mist AI Operations and Analytics Guide, the Coverage SLE is defined as:

“The percentage of client connections that meet the minimum signal strength threshold required for satisfactory performance. Sub-threshold values represent areas where coverage is inadequate or inconsistent.”

In the exhibit, the SLE visualization shows79% successful coverageand21% unsuccessful coverage, meaning that21% of connection attempts or client areas did not meet the desired signal strength threshold. These failures may be caused by factors such as:

Insufficient AP density or placement.

RF interference from neighboring devices.

Physical obstructions impacting signal propagation.

Thus, theunsuccessful coverage percentage is 21%, indicating potential areas where additional APs or configuration optimization may be needed to improve user experience.

ThePredictive Analytics and Correlation Engine (PACE)is the core AI engine that powers theJuniper Mist Cloud. It is designed to continuously learn from real-time telemetry and user interactions across wireless, wired, and WAN networks to proactively identify anomalies and predict performance issues.

According to theJuniper Mist AI Cloud and PACE Architecture Documentation, PACE:

“Leverages machine learning and advanced analytics to correlate data across millions of network sessions, automatically identifying patterns that affect user experience.”

The engine continuously evaluates key Service Level Expectations (SLEs), such asTime to Connect,Throughput, andRoaming, to establish performance baselines and detect deviations before users notice them.

PACE doesnot rely on manual analysisoruser feedback, and it integrates telemetry rather than external monitoring tools to maintain a closed-loop feedback system for self-correction and optimization.

Therefore, PACE usesmachine learning (A)to understand, predict, and improve end-user experience across Juniper Mist-managed networks.

TheJuniper Mist Cloudarchitecture is built on amicroservices-based design, which enhances reliability, scalability, and operational efficiency. Each microservice performs a specific function—such as authentication, telemetry ingestion, analytics, or AI inference—independently of other components.

According to theJuniper Mist Cloud Architecture and Operations Guide:

“Mist Cloud leverages a microservices architecture where each service operates independently. This design ensures that a failure in one service does not impact the functionality or availability of others.”

This modular approach provides several key advantages:

Fault isolation:One component can fail or restart without affecting the rest of the system.

Independent scaling:Services can scale dynamically based on demand.

Continuous updates:New features and patches can be deployed without downtime.

Options B, C, and D are incorrect because Mist microservices do not rely onNETCONFfor internal communication, arenot monolithic, and donot share a single common database—each maintains its own data model as required.

Therefore, the correct answer isA. Microservices are designed to be independent to ensure that if one process fails, the remaining processes will continue to operate.

The Juniper Mist REST API uses the JSON (JavaScript Object Notation) format for data exchange. The official Juniper Mist REST API overview states: “Juniper Mist uses REST APIs, which use HTTP methods (GET, POST, PUT, and DELETE) to transfer data in JavaScript Object Notation (JSON) format.” All interactions with the API involve sending request payloads in JSON, and receiving responses encoded in JSON as well. Juniper Mist’s automation and integration guides reinforce that the content type for API communications is always "application/json". Sample requests provided in the documentation, such as creation or feedback from POST, PUT, and GET methods, are displayed in JSON structure.

Unlike other formats such as YAML, XML, or protocol buffers, JSON is universally supported and highly favored for RESTful APIs because it is lightweight, human readable, and easily parsed by web and programming frameworks. This ensures consistency and compatibility across diverse integrations and automation workflows using the Mist platform.

To use Marvis Minis, both theWiFi Management and AssuranceandMarvis for Wirelesssubscriptions are required. According to the official Juniper documentation, Marvis Minis "does not require a separate subscription. Any organization with an active Marvis for Wireless subscription is automatically entitled for Marvis Minis support." However, the Marvis for Wireless subscription operates on top of the foundational WiFi Management and Assurance subscription, which is necessary to operate APs and the wireless network within the Mist Cloud. As stated: "In addition to your base Assurance subscriptions (Wireless Assurance, WAN Assurance, or Wired Assurance), you also need Marvis subscriptions for each device..." Therefore, both are prerequisites for using Marvis Minis, ensuring full wireless management with AI-powered digital twin validation.

Marvis, the AI-driven Virtual Network Assistant inJuniper Mist, uses continuous telemetry analysis and AI correlation to identifybad cable conditions. It does this by examining interface-level statistics gathered from switches and access points throughWired Assurance.

According to theJuniper Mist Marvis Operations and Troubleshooting Guide:

“Marvis detects bad cable issues by analyzing telemetry patterns such as CRC errors, FCS errors, packet drops, and link flaps that collectively match a known cable fault signature.”

When such patterns are detected, Marvis automatically generates anAI Actionindicating aBad Cableissue and identifies whether the problem is with theportor thecable. This proactive detection allows administrators to replace faulty hardware before it impacts network users.

Options A, B, and C are incorrect because Marvis does not perform active pinging, random packet captures, or random cable testing. Its detection is entirelyAI and telemetry-based.

Therefore, the correct answer isD. Marvis analyzes metrics, such as interface errors, that match a bad cable pattern.

The correct statement is that Juniper APs have a dedicated radio to scan for rogue and honeypot APs. The official Juniper Mist documentation specifies: "Juniper APs include a dedicated scanning radio to detect rogue, neighbor, and honeypot APs and their clients... The dedicated scanning radios operate on 2.4, 5, and 6 GHz Wi-Fi bands." This third radio operates independently from the client-serving radios and enables continuous threat detection on all Wi-Fi bands, enhancing security and allowing real-time performance adjustments. BLE radios are used for location-based services and asset tracking, not for rogue AP detection. Client-serving radios can also scan, but detection is always enabled and optimized by the dedicated radio.

In theJuniper Mist Cloud architecture, configuration hierarchy and inheritance are key design principles for managingEX SeriesandQFX Seriesswitches underWired Assurance. Configuration settings can be applied at both theorganization leveland thesite level, with clearly defined precedence rules.

According to theJuniper Mist Wired Assurance Configuration Guide, theorganization leveldefines global configuration templates that apply across multiple sites, while thesite levelprovides customization options specific to a particular location or environment.

The documentation states:

“Site-level configuration overrides any global or organizational template configurations for switches within that site.”

and further clarifies:

“Administrators can create or modify switch templates at the site level to apply custom settings to specific deployments without affecting other sites under the same organization.”

Thus:

Statement Cis correct — site-level configurations are an additional method to apply templates for a specific site.

Statement Dis correct — site-level configurationstake precedenceover org-level templates.

Statement Ais incorrect because org-level templates donotoverride site-level configurations; the reverse is true.

Statement Bis incorrect because site-level configuration isnot requiredfor manual switch setup—it is an optional, more efficient method of template application.