JN0-460 Exam Dumps - JNCIS-MistAI-Wired

In theJuniper Mist AI for Wired dashboard, administrators can select one or more switch ports (as shown in the exhibit, e.g., port ge-0/0/25 on an EX4100-48MP). Once configuration changes are made — such as VLAN assignment, port profiles, PoE settings, or administrative state — theSave buttonmust be clicked to confirm and apply those changes to the device.

“When making configuration changes in the Mist switch interface, the Save button must be used to confirm the modifications. Clicking Save applies the selected port settings to the switch through Mist Cloud.”

Option Ais incorrect: saving does not create a backup. Backups and snapshots are handled through Mist’s configuration archive, not via the Save button.

Option Bis incorrect: Save does not reset configuration; instead, it commits changes.

Option Cis incorrect: there is no preview function tied to Save.

Option Dis correct: the Save button is explicitly forapplying configuration changesto the selected switch or port(s).

EVPN, when used with VXLAN, leveragesBGP MPLS/VXLAN control plane mechanisms. To prevent overlapping IP prefixes between different tenants or sites, EVPN uses aRoute Distinguisher (RD).

“In EVPN-VXLAN, the route distinguisher (RD) makes routes unique when overlapping IP prefixes or MAC addresses are advertised between multiple tenants or sites.”

Option A(designated forwarder) applies to multi-homing in EVPN, not prefix uniqueness.

Option B(Layer 2 gateway) does not prevent IP overlap; it bridges VLANs.

Option D(ESI) is used for identifying multi-homed Ethernet segments, not to differentiate overlapping subnets.

Option C(Route Distinguisher) is correct, as it uniquely identifies routes even if the IP addresses are the same across sites.

According to Juniper’s Validated Designs (JVD) for campus fabrics, the Campus Fabric IP Clos (often referred to as a 5-stage Clos) is the architecture specifically engineered for high-scale environments supporting 10,000+ users. This design is characterized by extending the EVPN control plane and VXLAN data plane all the way to the access layer. By terminating VXLAN tunnels at the access switch—functioning as a Virtual Tunnel Endpoint (VTEP)—the network can seamlessly support both Layer 2 and Layer 3 devices directly at the edge of the fabric.

The primary driver for choosing IP Clos in large-scale deployments is its superior scalability. In this model, the core switches are shielded from the massive table sizes required to learn thousands of individual endpoint MAC and IP addresses. Instead, core devices only need to maintain reachability to the loopback addresses of the access layer switches. This reduction in the "flood and learn" pressure on the core is critical as the number of endpoints grows, ensuring that the core can scale far into the future with predictable growth.

Additionally, the IP Clos architecture provides the most flexible environment for microsegmentation using Group-Based Policies (GBP) and macrosegmentation using Virtual Routing and Forwarding (VRF) instances. Since routing occurs at the access device, "east-west" traffic between local endpoints remains efficient and does not need to traverse the distribution or core tiers. While EVPN Multihoming and Core-Distribution (CRB/ERB) models are effective for smaller sites or brownfield environments where access switches are legacy Layer 2-only devices, only the IP Clos architecture provides the end-to-end VXLAN capability required for high-density, future-proof campus fabrics.

According to Juniper Mist documentation, Wired Service Level Expectations (SLEs) are designed to provide operational visibility into the wired experience by monitoring Juniper EX and QFX Series switches. These SLEs leverage pre-connection and post-connection performance metrics to ensure network reliability and performance.

Pre-connection metrics are specifically focused on the initial phase of a client's journey to join the network. These metrics show the total number and the time required for successful connects and authentication. By tracking 802.1X events and DHCP snooping data, Mist can identify if clients are failing to onboard due to RADIUS issues, authentication timeouts, or failures in the DHCP binding process. This allows administrators to proactively address onboarding hurdles before they impact the broader user base.

Post-connection metrics, conversely, measure the network experience once a client is already "on the wire" and attempting to pass data. These metrics primarily measure throughput and focus on detecting various network errors and operational anomalies. Specifically, post-connection monitoring detects issues such as Spanning Tree Protocol (STP) loops, interface errors, and congestion. Additional post-connection insights include identifying cable issues, negotiation failures, and MTU mismatches that might occur during active data transmission.

In summary, Juniper Mist divides its telemetry into these two distinct categories to simplify troubleshooting. Pre-connection metrics ensure that users can get on the network, while post-connection metrics ensure that the network can sustain their traffic requirements without errors. This combined approach enables the Mist AI-driven Predictive Analytics and Correlation Engine (PACE) to provide a holistic view of the end-user experience, moving beyond traditional "up/down" monitoring to a deeper understanding of the quality of the client experience.

Marvis, the AI-driven virtual network assistant in Mist, provides real-time troubleshooting insights for both wired and wireless devices.

When used withWired Assurance, certain actions require Juniper EX or QFX switches for telemetry collection.

Two of these wired-specific actions are:

Bad Cable Detection– triggered when electrical faults or poor cable quality are observed through LLDP or physical diagnostics.

According to Juniper Mist documentation, the EX2300, EX4650, and QFX5120 switches require a specific additional workflow within the Mist portal to form a Virtual Chassis because these models do not have dedicated Virtual Chassis ports (VCPs). Unlike other EX-series models (such as the EX4100 or EX4400) that possess factory-defined, dedicated VCPs and can form a Virtual Chassis automatically upon physical connection, these three platforms require an administrator to explicitly initiate the "Form Virtual Chassis" action from the Mist UI.

This workflow is necessary because the system must logically convert standard network uplink ports into Virtual Chassis ports to enable stack communication. To execute this, the switches must first be onboarded to the Juniper Mist cloud as standalone devices and assigned to the same site. Once they are online and their configuration is managed by Mist, the administrator selects the desired members from the switch inventory and clicks More > Form Virtual Chassis.

During this additional workflow, the Mist portal prompts the user to specify which front-panel ports will be repurposed as VCPs and allows for the manual assignment of member IDs and Routing Engine roles. This "pre-provisioned" method ensures a deterministic setup where roles are not left to chance. Once the command is pushed, the switches reboot to reconfigure their internal hardware logic to treat the selected ports as fabric links. Juniper emphasizes that for these specific models, the "Form Virtual Chassis" option is the only supported way to establish the stack within Wired Assurance, as it ensures that the cloud-native management plane correctly recognizes the multiple physical chassis as a single logical entity.

According to Juniper Mist documentation, the Throughput Service Level Expectation (SLE) is the primary metric used to measure the ability of wired clients to pass traffic across the physical network without impedance. This SLE is critical for diagnosing issues with real-time, high-bandwidth applications such as video streams, which are highly sensitive to packet loss and latency. Within the Throughput SLE, the Storm Control classifier is specifically designed to identify "bad user minutes" caused by the suppression of Broadcast, Unknown Unicast, and Multicast (BUM) traffic.

Storm control is a mechanism that enables the switch to monitor traffic levels and drop BUM packets when a specified traffic level—known as the storm control level—is exceeded. This prevents a "traffic storm" from proliferating and degrading the overall performance of the LAN. While this is a vital security feature to prevent network meltdowns, it can inadvertently impact legitimate traffic. For instance, if multicast-based video streams or other heavy BUM traffic exceed the configured bandwidth percentage on a port, the switch will drop those packets to protect the rest of the network.

When troubleshooting video stream issues, network administrators should inspect the Storm Control classifier to see if it is triggering "bad user minutes". If the Mist dashboard indicates failures under this classifier, it signifies that the switch hardware is actively dropping packets because the BUM traffic limit has been reached. This provides immediate root-cause evidence, allowing the administrator to determine if they need to adjust the storm control thresholds within the Port Profile or investigate the source of the excessive broadcast traffic. By correlating these hardware-level events with the end-user experience, Mist AI simplifies the resolution of complex performance problems that traditional "up/down" monitoring would miss.

Juniper Mist AI for Wired uses group-based policy (GBP) to enforce segmentation within a single VRF, without requiring separate VRFs per user group. GBP tags classify endpoints logically (e.g., employees, guests, IoT devices) and enforce security rules across the EVPN-VXLAN fabric.

“Group-Based Policy (GBP) provides scalable segmentation by assigning tags to endpoints based on attributes such as user type, role, or device type. These GBP tags are then used in policies to control communication between groups.”

Option A is inefficient: assigning unique tags per endpoint is not scalable.

Option C is incorrect: GBP is identity-based segmentation, not location-based.

Option D is incorrect: assigning a single tag defeats the purpose of segmentation.

Option B is correct: the best practice is to assign GBP tags based on endpoint type (e.g., servers, staff, IoT, guests), which then drives policy enforcement.