For the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) exam, monitoring remote workforces requires a strategy that captures both the user ' s local environment and the regional internet health. In a scenario involving Cisco AnyConnect VPN, the " last mile " connectivity of the employee is often the most significant variable in application performance.

Utilizing the ThousandEyes Endpoint Agent (Option D) is the most effective way to monitor this environment. Because the agent resides directly on the remote employee ' s machine, it can monitor the performance of the web application both " inside " and " outside " the VPN tunnel. It provides visibility into the local Wi-Fi signal strength, the health of the AnyConnect client, and the latency experienced as traffic traverses the VPN headend. This allows engineers to differentiate between a slow home internet connection and an issue with the VPN concentrator.

Deploying ThousandEyes Cloud Agents (Option A) serves as a critical baseline. By running tests from Cloud Agents in the same regions as the remote employees, the engineer can determine if the " internet " in that region is healthy. If a Cloud Agent in London shows a perfect response time while an Endpoint Agent in London shows high latency, the engineer can immediately isolate the problem to the user ' s specific setup or the VPN path, rather than a regional ISP outage.

Other options are less suitable for monitoring the remote employee ' s experience :

AppDynamics (Option B) provides server-side code visibility but cannot see the user ' s home Wi-Fi or local network path.

Enterprise Agents on the VPN concentrator (Option C) can monitor the path from the data center to the app, but they cannot see the path from the user to the concentrator.

Enterprise Agents in the data center (Option E) provide an " inside-out " view of the app ' s health but miss the entire remote access experience.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, the evolution of network monitoring includes moving from periodic polling to real-time data streaming. The exhibit displays a curl command targeting the ThousandEyes API v7 /stream endpoint. When integrating ThousandEyes with high-performance observability platforms like Grafana , the standardized and recommended method for machine-to-machine data exchange is through OpenTelemetry (OTel) .

According to the ENNA architecture guidelines, the ThousandEyes Streaming API allows users to push granular test metrics (such as network latency, packet loss, and jitter) to external collectors in an OTel-compatible format. In the provided JSON payload, the " type " field is a mandatory parameter that defines the integration protocol. For Grafana, which natively supports OpenTelemetry Protocol (OTLP) via its OpenTelemetry Collector, the value must be set to " opentelemetry " (Option A). This tells the ThousandEyes streaming engine to encapsulate the data according to the OTel semantic conventions, ensuring that Grafana can correctly interpret and visualize the metrics without additional custom parsing logic.

While other options exist in the ThousandEyes ecosystem, they do not fit the specific API call shown for this use case:

Custom Webhooks (Option B) are typically used for event-driven alerts and notifications (e.g., sending a POST request when a threshold is breached) rather than continuous high-fidelity metric streaming.

Push-api and poll-api (Options C and D) are not valid " type " values within the context of the v7 /stream endpoint, as the streaming service specifically utilizes the OpenTelemetry framework for real-time delivery.

By selecting opentelemetry , the network engineer enables a robust " push-based " integration that provides real-time visibility into application performance and network health, leveraging Grafana ' s advanced dashboarding capabilities to analyze ThousandEyes telemetry data alongside other enterprise infrastructure metrics.

Introduction to ThousandEyes for OpenTelemetry

This video provides a foundational understanding of how ThousandEyes uses modern streaming frameworks to export critical performance data to external observability platforms.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, isolating performance issues on end-user machines requires a combination of application-layer experience data and local system telemetry. When an architect suspects that local hardware constraints, such as high CPU usage, are degrading the digital experience, the Endpoint Agent is the required vantage point.

The correct combination is Real User Tests > Endpoint, CPU utilization ≥ % (Option C). This alert configuration is effective for several reasons:

Targeted Visibility: It directly monitors the user ' s device (Endpoint) where the hardware bottleneck is suspected to reside.

Real-Time Context: By utilizing Real User Tests (RUM) , the agent gathers performance data during actual user activity (e.g., browsing the web application). This ensures that the CPU spikes are correlated with active application usage, providing a representative view of the impact on experience.

Threshold Alerting: The condition monitors for CPU utilization exceeding a defined percentage threshold (≥ %). This allows IT teams to be notified only when the CPU load reaches problematic levels that are known to cause application sluggishness or browser " freezing " .

Alternative options are less effective for this specific troubleshooting goal:

Option A: Monitors network loss, which would not identify a local CPU bottleneck.

Option B: Monitors path length (hops), which is a routing metric irrelevant to local hardware performance.

Option D: Monitors memory load during scheduled tests. While memory is a factor, the specific suspicion in this scenario is CPU utilization, and scheduled tests do not always capture the same load impact as a real user interacting with the browser.

According to the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, the native integration of ThousandEyes within the Meraki portfolio is specifically focused on the Meraki MX security and SD-WAN appliances. This integration allows users to install ThousandEyes Enterprise Agents directly on supported Meraki MX hardware, providing critical visibility into the performance of services that distributed users rely on. 4

The Meraki MX (Option A) supports the ThousandEyes Enterprise Agent as a containerized service that can be activated with a single click within the Meraki Dashboard. 5 This integration is designed to provide better monitoring and testing capabilities for customers interested in improving the quality of their experience and adding the appropriate SD-WAN policies to optimize network performance. By running the agent natively on the MX, administrators can monitor the performance of external applications and services directly from the SD-WAN sites, bridging the gap between local branch health and global internet performance. 6

While Cisco continues to expand its assurance integrations across the portfolio, the MR (Wireless) , MS (Switching) , MV (Camera) , and MG (Cellular) series do not currently support the native, " on-box " execution of the ThousandEyes Enterprise Agent. For these other platforms, visibility is typically achieved through passive monitoring via Meraki Insight (MI) or by deploying ThousandEyes agents on connected client devices (Endpoint Agents) or nearby infrastructure. Therefore, the Meraki MX is the only verified platform supporting native ThousandEyes agent deployment in this context.