Pass the Juniper JNCIA-MistAI JN0-253 Questions and answers with CertsForce

The fundamental sampling method used in supervised learning is a labeled dataset. As described in Juniper Mist AI and machine learning application guides, supervised learning algorithms are trained using datasets in which each sample is paired with a known output (label). The model is fed examples comprising input data and the correct answer, learning patterns to map new inputs to their target outputs. While regression, decision tree, and random forest are techniques within supervised learning, the process fundamentally relies on having a labeled dataset for both training and validation phases. “Supervised machine learning models are built on labeled data, with each input paired to a target value or classification.” This allows for accurate model training and evaluation.

Juniper Mist Access Assuranceprovidescloud-based network access control (NAC)by integratingRADIUS,identity-based policy enforcement, andWxLAN role-based segmentation. It allows administrators to assign users and devices to specific VLANs or network segments dynamically based on authentication attributes, thereby improving bothsecurityandpolicy granularity.

According to theJuniper Mist Access Assurance Configuration Guide, to enhance network security in a multi-VLAN environment, you should:

“Use EAP-TLS (certificate-based authentication) for secure user identification, and dynamically assign WxLAN roles or VLANs based on Active Directory (AD) group membership.”

Here’s whyDis correct:

EAP-TLSensuresmutual authenticationbetween client and RADIUS server using digital certificates — preventing credential theft or spoofing.

Integrating withActive Directoryallowsrole-based access control (RBAC), enabling dynamic VLAN or WxLAN assignment. This ensures users from different groups (e.g., staff, contractors, guests) are automatically segmented into appropriate network zones.

Option breakdown:

A (MAB): Used for non-802.1X devices (e.g., printers), less secure for user authentication.

B (EAP-TTLS): Username/password-based and less secure than certificate-based EAP-TLS.

C (Host authentication): Verifies devices only, not user identity—insufficient for user-level control.

Therefore, the most secure and scalable configuration is:

✅D. Configure user authentication using EAP-TLS and assign WxLAN roles based on AD user group.

When an administrator begins to open asupport ticketwithin theJuniper Mist portal, theMarvis Virtual Network Assistantproactively assists by presenting relevant, AI-driven recommendations and documentation.

According to theJuniper Mist Marvis Virtual Network Assistant Guide, this process is described as:

“While an administrator creates a support case, Marvis automatically searches the Mist knowledge base and presents probable causes and recommended solutions based on the detected issue type.”

If the issue involves anAP that does not power on, Marvis retrieves contextual data such as PoE status, switch port state, and recent telemetry logs, then providesautomated guidance—for example, checking PoE configurations or cable connectivity.

Thus, while the administrator fills in the “New Ticket” details,Marvis provides potential solutions directly from Juniper Mist’s documentation, potentially resolving the issue before a support case is even submitted.

The correct answer isD. Marvis provides possible solutions to AP power problems from the Juniper Mist documentation.

Audit Logs in Juniper Mist are used to view a complete record of logins. The documentation states: "On the Audit Logs page, you can see who logged in to the Juniper Mist portal, when they logged in, and what they did." Audit Logs also record configuration and administrative actions by users on all sites within the organization, enabling administrators to track activity for compliance, troubleshooting, and security. This comprehensive record assists with accountability and change tracking, but they do not manage subscriptions, organization setup, or error logs for all devices. Filtering options allow for activity analysis over selectable time periods, by users, and by site to provide detailed insights into administrative behavior and user access history.​

Juniper Mist Location Services provide asset location and wayfinding. According to official Juniper documentation: "Juniper Mist provides location accuracy for wayfinding, asset visibility, and user engagement," leveraging cloud-native vBLE and Wi-Fi technologies.

Asset location enables organizations to locate high-value resources, personnel, or IoT devices in real time with rich analytics, making it easy to find assets and people using the Mist dashboard and APIs.

Wayfinding gives users turn-by-turn indoor navigation and mapping via mobile SDKs and the Mist cloud.While geofencing can be achieved as part of location-based engagement features, it is not listed as a primary Mist Location Service solution. GPS location is not directly supported; Mist Location Services operate indoors using Wi-Fi and BLE signals, not satellite GPS.

TheMist AI Efficacy Loopis a key concept within theJuniper Mist AI architecturethat ensures continuous improvement of itsmachine learning (ML)andartificial intelligence (AI)models. This process combines feedback and data collected from multiple sources — including real-world network telemetry, customer success feedback, domain experts, and the Mist data science team — to refine the AI algorithms and enhance prediction accuracy.

According to theJuniper Mist AI Operations and Architecture Guide, the process is described as follows:

“The Mist AI efficacy loop is a closed-loop feedback system that continuously collects data from the field, correlates it with expert knowledge and support insights, and uses it to retrain and improve AI models for greater accuracy and reliability.”

This iterative learning cycle allows Mist AI to:

Improve anomaly detection precision.

Reduce false positives in network issue detection.

Enhance Marvis’s ability to accurately identify root causes and recommend corrective actions.

Theefficacy loopis what differentiates Mist AI from static analytics platforms, as it creates aself-learning systemthat evolves with every deployment and customer interaction.

Therefore, the correct answer isB. Mist AI efficacy loop.

When a customer requests asubscription renewalfor Juniper Mist services via e-mail, the process is handled through a coordinated workflow between theJuniper Mist Cloud licensing systemandJuniper Support. The renewal request ensures that existing service entitlements—such asWireless Assurance,Wired Assurance,WAN Assurance,Marvis AI, orPremium Analytics—continue without interruption.

According to theJuniper Mist Cloud Licensing and Subscription Management Guide, the renewal workflow is described as follows:

“When a renewal request is submitted via e-mail, Juniper Mist support receives and validates the request, initiates processing for the selected subscriptions, and issues a new activation code once the renewal is completed.”

Here’s what happens after the renewal request:

B.The Juniper Mist systemsends a confirmation e-mailacknowledging the renewal request and routes it tosupport for processing.

C.Once processed,Juniper Mist generates and delivers an activation codevia e-mail that must be entered into the Mist portal to activate the renewed subscription(s).

Incorrect options:

A:No promotional materials or physical packages are sent.

D:A cancellation notice is not sent for renewal requests; it applies only to expired or terminated subscriptions.

Therefore, the correct answers areB. Juniper Mist e-mails the support request for the selected subscriptionsandC. Juniper Mist processes the order and sends an activation code.

A DHCP lease must be obtained by the switchbefore the ZTP process proceeds. Juniper documentation clarifies: "Ensure that a DHCP server is reachable. In addition to providing an IP address to the switch, your DHCP server must provide the following information: a default gateway, an NTP server, and a DNS server address..." The initial event in the ZTP workflow is the switch receiving a valid IP address via DHCP. Only after obtaining an address can the switch connect to the Mist cloud and proceed with zero-touch provisioning. Other requirements, such as user account, NETCONF, or NTP sync, occur later or are handled automatically via cloud policy after onboarding.

TheMarvis Virtual Network Assistant (VNA)in Juniper Mist AI uses real-time telemetry and AI-driven correlation to identify hardware and connectivity issues, including bad cable conditions. When a“Bad Cable”alert is raised, Marvis analyzes physical link metrics from the switch and AP—such as CRC errors, FCS drops, and link flaps—to isolate the root cause.

According to the Juniper Mist AI Operations Guide, theMarvis Actions dashboardprovides contextual insights into the issue, showing whether the fault lies in thephysical cableitself or theswitch portit is connected to. Specifically, the dashboard highlights:

The affected device and port.

Whether the degradation originates from theport hardwareor thecabling.

Suggested corrective action (e.g., replace the cable or check switch hardware).

Marvis uses its AI-driven root cause analysis engine to distinguish between cable integrity problems and port failures by examining electrical signature anomalies and historical telemetry.

Options B, C, and D are incorrect because Marvis does not display cable category ratings or identify individual broken Ethernet pairs—it focuses on identifying and isolating the fault domain.

Therefore, the correct answer isA. If the port or cable is at fault.

In the Juniper Mist dashboard,Service Level Expectations (SLEs)are metrics that measure user experience in key areas such as connection, throughput, and roaming. Each SLE is composed ofclassifiers, which help identify the underlying cause of degraded performance or sub-threshold scores.

According to the Juniper Mist AI documentation, theRoaming SLEtracks client transitions between access points and evaluates the quality of those roaming events. The contributing classifiers typically includeSignal Quality,Wi-Fi Interference,Ethernet, andCapacity.

In this exhibit, the bar forCapacityis the longest under the “Roaming Classifiers” section, indicating that it has the most significant impact on theSub-ThresholdSLE value (10.6%). This means roaming performance is primarily being limited by insufficient capacity — often due to AP radio congestion or a high number of concurrent clients impacting handoff efficiency.

Hence, theCapacityclassifier is responsible for the sub-threshold SLEs.