The network team has deployed Webex RoomOS Endpoint Agents and integrated Webex ControlHub with ThousandEyes. The VoIP team wants to know which metrics they can collect from the Webex Control Hub view.Where does the VoIP team find the network data?
Answer(s): B
According to the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, the integration between ThousandEyes and Webex Control Hub provides a streamlined troubleshooting experience for collaboration services. For the VoIP team to access the specific ThousandEyes network telemetry--such as latency, loss, and jitter--they must navigate to the Network Path (Option B) section within the Troubleshooting tab of the Control Hub.15The Network Path visualization is a direct result of the ThousandEyes Endpoint Agent data being pulled into the Webex interface.16 When a user or RoomOS device experiences poor audio or video quality during a meeting, the Control Hub's troubleshooting view displays a "Network Path" line under the participant's details.17 By clicking on this line, the VoIP team can see a hop-by-hop breakdown of the entire route from the collaboration device to the Webex media node. This view highlights specific hops where performance is "Poor" (red), "Fair" (yellow), or "Good" (green) based on predefined thresholds for latency (>400ms) or loss (>5%).While "Devices" (Option A) is where the agents are activated, and "Users" (Option C) allows for selecting a specific participant, the actual telemetry metrics and the visualization of the network route are strictly located in the Network Path view. This integration eliminates the need for the VoIP team to leave the Webex environment for initial triage, as they can identify if a problem is local to the branch office or deep within a service provider's network directly from the "Network Path"dashboard.
A network administrator wants to establish a baseline for CPU utilization on their core routers.Which data source would be MOST appropriate for this purpose?
Answer(s): C
In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) framework, baselining is the process of establishing a "normal" performance profile for network infrastructure to enable the detection of anomalies.When the metric of interest is the internal health of a physical device, such as CPU utilization on a core router, SNMP (Simple Network Management Protocol) is the industry-standard data source.SNMP provides direct visibility into the device's control plane and hardware performance. By polling specific Object Identifiers (OIDs) from the router's Management Information Base (MIB), a monitoring system like Cisco Catalyst Center or a third-party NMS can collect granular data on CPU cycles, memory allocation, and temperature. This "inside-out" telemetry is essential for baselining because it reflects the actual resource consumption of the router during various traffic loads.Conversely, ThousandEyes tests (Options A, B, and D) provide "outside-in" synthetic data.While DNS resolution time (Option A), HTTP response times (Option B), and Path Visualization (Option D) are excellent for measuring end-to-end service delivery and network transit health, they do not report on the router's internal hardware state. For instance, a router could have 99% CPU utilization (indicating a potential crash), yet a ThousandEyes path test might still show a "green" path if the data plane (ASICs) is still forwarding packets efficiently. Therefore, to establish a reliable baseline for hardware-specific metrics like CPU, SNMP data (Option C) is the only appropriate source among the choices.
What is an important consideration when choosing a time period for collecting data to establish a baseline for interface utilization on a critical network link?
Establishing an accurate baseline is a cornerstone of the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) methodology. A baseline must represent the "typical" behavior of the network across its standard operating cycle to be useful for anomaly detection and capacity planning.When monitoring interface utilization on a critical link, it is vital to capture both peak and off-peak traffic patterns (Option C).Most enterprise networks exhibit cyclical traffic patterns, often referred to as "diurnal" cycles, where utilization spikes during business hours (peak) and drops significantly at night or on weekends (off- peak). If an engineer only collects data during the lowest volume period (Option A), the baseline will be unrealistically low, leading to "false positive" alerts when normal business traffic returns. Conversely, only capturing peak data might hide background synchronization tasks or backup windows that occur during off-hours.By selecting a representative time period--typically one full week or a month--the engineer ensures the baseline accounts for variations such as Monday morning "logon storms," mid-week steady states, and weekend maintenance. This comprehensive view allows the assurance platform (such as Cisco Catalyst Center or ThousandEyes) to calculate a standard deviation and establish dynamic thresholds. If utilization deviates from these historically representative norms, the system can trigger an alert based on a true anomaly rather than a predictable daily surge. Aligning with a financial year (Option B) is irrelevant to technical performance, and limiting the data (Option D) compromises the baseline's statistical validity.
Your organization wants to be notified of an event as soon as it is triggered by an alert threshold. This notification should be sent to your ITSM and generate an incident so it can be responded to appropriately.What kind of integration should you use?
Answer(s): A
In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, the objective of modern network assurance is to bridge the gap between "visibility" and "action".When an organization requires an automated workflow to handle network performance anomalies, the most efficient architecture is the native ServiceNow Integration (Option A). This integration is categorized as a "Custom-Built" or native integration within the ThousandEyes platform, designed specifically to facilitate the delivery of direct notifications into a ServiceNow account.According to the ENNA implementation standards, the ThousandEyes ServiceNow integration utilizes the ServiceNow Incident Management module.When a predefined alert rule (such as a 5% packet loss threshold on a critical SaaS path) is violated, ThousandEyes triggers an event and immediately pushes the alert data to ServiceNow via an OAuth-authenticated connection. Within ServiceNow, this data is used to automatically generate an Incident, complete with relevant metadata such as the test name, agent location, and the specific metrics that triggered the violation. This automation eliminates the manual overhead of "copy-pasting" alert details from a monitoring dashboard into a ticketing system, thereby significantly reducing the Mean Time to Identification (MTTI).While Custom Webhooks (Option C) can achieve a similar result by sending JSON payloads to a REST API, they require additional development effort to parse the data on the receiver side. The native ServiceNow integration provides a pre-configured template that maps ThousandEyes alert fields directly to ServiceNow incident fields, offering a "one-click" setup experience that is preferred for enterprise-grade deployments. Options B and D are irrelevant for the specific goal of ITSM incident generation. Therefore, for direct ITSM notification and incident creation, the native ServiceNow Integration is the verified recommendation.
You have been tasked with creating a dashboard in your organization's Observability platform. This dashboard should have data that is streamed in real-time and used to populate data for tables, graphs, charts, and other formats.What kind of integration should you use?
Within the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) framework,the transition from "polling" to "streaming" is a major architectural shift. To populate real-time dashboards in external observability platforms like Grafana, Splunk, or AppDynamics, the architect should utilize the OpenTelemetry (OTel) integration (Option B).ThousandEyes for OpenTelemetry is a push-based API built on the standardized OpenTelemetry Protocol (OTLP). Unlike traditional REST API polling (Option A), which retrieves data at fixed intervals and can be subject to rate limiting and latency, the OTel integration allows ThousandEyes to stream granular network metrics as they are collected. These metrics--including latency, loss, jitter, and HTTP response times--are exported in a standardized format that is natively understood by modern observability backends. This allows the platform to populate complex visualizations such as time- series graphs, heatmaps, and multi-metric tables in near real-time, providing a "single pane of glass" view that correlates network performance with application and infrastructure telemetry.A key advantage of the OTel approach is data portability and correlation. By applying metadata tags to ThousandEyes tests, the data can be filtered and categorized within the external dashboard to match the organization's business logic (e.g., grouping by region or application tier). This enables SREs and NetOps teams to quickly identify if a performance dip in an application dashboard correlates with a spike in internet latency measured by ThousandEyes. Options C and D do not provide the streaming data pipeline required for real-time external dashboard population. Thus, OpenTelemetry is the definitive choice for high-fidelity, real-time observability integration.
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