Cisco 300-445 - Designing and Implementing Enterprise Network Assurance(300-445 ENNA)

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, the native integration of ThousandEyes within the Meraki portfolio is specifically focused on theMeraki MXsecurity 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

TheMeraki MX (Option A)supports the ThousandEyes Enterprise Agent as a containerized service that can be activated with a single click within the Meraki Dashboard.5This 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, theMR (Wireless),MS (Switching),MV (Camera), andMG (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 viaMeraki Insight (MI)or by deploying ThousandEyes agents on connected client devices (Endpoint Agents) or nearby infrastructure. Therefore, theMeraki MXis the only verified platform supporting native ThousandEyes agent deployment in this context.

In the framework ofDesigning and Implementing En8terprise Network Assurance (300-445 ENNA), selecting the correct agent type depends heavily on the vantage point required for the specific observation. For this scenario, the architect must collect metrics from theinternal network access layer—the point closest to where the users or devices reside within the corporate perimeter—towards a cloud-hosted destination.

TheEnterprise Agent(Option B) is the most appropriate choice because it is specifically designed to be deployed on infrastructure owned and managed by the organization. These agents are "inside-out" vantage points that can be installed directly onCisco Catalyst 9300 or 9400 Series switchesat the access layer using Docker containers. By deploying an Enterprise Agent at the access layer, the architect gains visibility into the entire network path, starting from the internal LAN, traversing the edge/WAN, and reaching into the cloud-hosted web server. This allows for the identification of issues such as local congestion, ISP peering problems, or cloud provider latency.

Other options do not meet the criteria:

Synthetic Agent (Option A):This is a distractor term. All ThousandEyes agents (Cloud, Enterprise, and Endpoint) are synthetic agents because they all perform active synthetic testing.

Cloud Agent (Option C):These are pre-deployed by Cisco in global ISP data centers and provide an "outside-in" view.14While useful for monitoring public-facing availability, they cannot provide visibility into the internal network or the access layer of the organization.

Endpoint Agent (Option D):While these are installed on end-user machines and provide a "user-centric" view, they are generally not used for infrastructure-level path analysis from the access layer switches themselves.

Thus, theEnterprise Agentis the definitive choice for monitoring from the access layer to the cloud.

TheDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)certification emphasizes that the primary value proposition of the Cisco "Assurance Stack" is the reduction ofMean Time to Identification (MTTI)andMean Time to Resolution (MTTR). The integration of ThousandEyes across Cisco's portfolio—including Catalyst, Meraki, and SD-WAN—allows for quick identification and resolution of performance issues(Option D).12

By embedding ThousandEyes agents into the existing network infrastructure, Cisco enables "end-to-end visibility" that spans domains the enterprise traditionally does not control, such as the public internet and SaaS environments.13During troubleshooting, this cross-platform visibility allows network engineers to immediately correlate internal network health (from Catalyst Center or Meraki Dashboard) with external path visualization (from ThousandEyes). For example, when a user experiences poor video quality in a Webex meeting, the integration allows the engineer to "cross-launch" from the Webex Control Hub directly into a ThousandEyes path view.14This pinpoint accuracy avoids the "blame game" between network, application, and ISP teams by providing a "single source of truth" regarding where the packet loss or latency is occurring.

While some automation exists (Option C), the core benefit isn't automatic configuration changes based on feedback, but rather providing theactionable insightsrequired for manual or policy-based remediation. Similarly, while it streamlines data gathering, its ultimate purpose is thespeed of resolutionin complex, hybrid environments.

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)data analysis guidelines, identifying the underlying test type is the first step in interpreting a dashboard. By observing the metrics displayed across both the Executive and IT Operations dashboards, we can definitively identify the test type asPage Load(Option B).

A Page Load test is a Web-layer test that uses a browser engine (Chromium) to fully render a page and collect advanced metrics beyond simple availability. Evidence for this test type in the dashboards includes:

Page Completion Time:Displayed on the Executive dashboard map, this metric is specific to how much of the page successfully rendered.

DOM Load Time:Found in the IT Operations dashboard, the Document Object Model (DOM) time is a browser-centric metric that measures when the page structure has finished loading.

Waterfall Charts:While not a specific answer option, these are the foundation of Page Load tests, allowing for the timing of individual web components.

SimpleHTTP Servertests (Option A) only measure availability and response codes, missing the rendering metrics seen here.Agent to server(Option C) is a network-layer test that provides path visualization but no browser-level data.FTP(Option D) is a protocol-specific test not used for web application monitoring. Therefore, the presence of DOM and Page Completion metrics confirms thePage Loadtest type.

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, capacity planning and optimization are driven by telemetry data such as NetFlow. When NetFlow identifies that delay and jitter—metrics highly impactful to real-time traffic—spike during peak hours, it indicates that high-priority packets are competing for resources with bulk data.

The most appropriate recommendation is totune the existing QoS configuration(Option C). This involves adjusting the Queuing and Scheduling policies on the routers to ensure that voice and video traffic (typically marked with EF and AF41/AF42 DSCP values) is serviced before other traffic classes during periods of congestion. This solution is targeted, cost-effective, and directly addresses the observed jitter issues without the need for massive capital expenditure.

Reviewing other options:

Option A:A complete QoS redesign is often unnecessary and too invasive for solving peak-hour jitter if a basic QoS framework is already in place.

Option B:Increasing bandwidth on "all" links is a "brute force" approach that is expensive and fails to address the underlying problem of traffic prioritization.

Option D:Hardware replacement is a last resort and would not resolve delay/jitter if the new hardware still lacks a properly tuned QoS policy.

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum,ThousandEyes WAN Insightsis a predictive analytics solution designed to transform network management from a reactive to a proactive model.10In the scenario provided, the voice quality issues are recurring and peak-hour dependent, indicating a need for optimization rather than just standard real-time alerting.11

The most relevant capability for this scenario isanalyzing historical SD-WAN performance datato generatepath recommendations(Option B). WAN Insights integrates withCisco Catalyst SD-WAN Manager(vManage) andvAnalyticsto ingest massive volumes of telemetry.12It uses advanced statistical models to analyze the performance of all active circuits (MPLS, Internet, etc.) over time. If the system identifies that a different available path consistently delivers a higher Quality of Experience (QoE) or better adherence to the voice SLA during those peak windows, it generates a recommendation to adjust theApplication-Aware Routing (AAR)policy.1314

This predictive approach allows the administrator to fine-tune network policies in advance, ensuring that sensitive traffic like voice is automatically rerouted to the most stable path before the congestion impacts the users. Option A describes standard ThousandEyes synthetic testing, while Option C is incorrect because WAN Insights specifically uses existing SD-WAN telemetry rather tha15n generating its own synthetic voice16probes. Option D and E describe general NMS or security features not specific to WAN Insights' predictive mission. Thus, the proactive recommendation of alternative paths based on historical SLA adherence is the core function of WAN Insights for improving voice quality.171819

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, the integration between ThousandEyes andWebex Control Hubprovides 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 theNetwork Path(Option B) section within the Troubleshooting tab of the Control Hub.15

TheNetwork Pathvisualization is a direct result of the ThousandEyes Endpoint Agent data being pulled into the Webex interface.16When 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.17By 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 actualtelemetry metricsand the visualization of the network route are strictly located in theNetwork Pathview. 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.

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, monitoring modern APIs often requires handling complex authentication flows, such as OAuth 2.0 with specific client credential parameters. The provided exhibit illustrates theHTTP Authenticationoptions available within a standard HTTP Server test: None, Basic, NTLM, Kerberos, and OAuth.

According to the ENNA implementation standards, while the HTTP Server test type supportsOAuth(Option C), its native implementation is limited. Specifically, it is designed to use a pre-existing token or a simple token refresh flow; it does not support the injection of custom parameters in the initial POST request required to obtain a token in many enterprise client-credential scenarios. Basic and NTLM (Options A and B) are legacy protocols that rely on simple username/password headers and cannot facilitate the multi-step token exchange process described.

To fulfill the requirement of a two-step flow (POST for token, followed by GET for resource), engineers must use more flexible test types:

Transaction scripts (Option D):These allow the engineer to write custom JavaScript using the ThousandEyes transaction library to programmatically handle the POST request, parse the resulting JSON token, and then pass that token into the subsequent GET request's header.

API tests (Option E):These are purpose-built for API monitoring and natively support the definition of variables and multi-step requests where the output of one call (the token) serves as the input for the next.

By utilizing these advanced test types, the engineer can successfully navigate complex authentication requirements that the standard HTTP Server test cannot accommodate.

The architecture forDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)specifies that ThousandEyes WAN Insights relies on deep integration with the Cisco SD-WAN management stack. To generate its predictive path recommendations, the platform must ingest specific telemetry data that reflects both the network's behavior and the applications traversing it.

The first essential data source ishistorical network performance metrics collected by vAnalytics(Option B). Before WAN Insights can be activated, vAnalytics must be enabled to collect and enrich raw network telemetry from the edge routers.34This data includes granular metrics for every SD-WAN tunnel, such as packet loss, latency, and jitter.35WAN Insights analyzes these historical trends to forecast future path quality and determine which transport circuits are most likely to meet application SLAs over a long-term period.

The second essential data source isapplication traffic flow data(Option D). WAN Insights must understand which applications are currently active in the fabric to prioritize recommendations for "business-critical" services like Office 365, Webex, or custom internal apps.38This information is ingested as flow records from the SD-WAN data plane and categorized based on theApplication Listsdefined inCisco Catalyst SD-WAN Manager (vManage).

Options A and E are configuration or logging data that, while useful for general management, are not the raw telemetry inputs used by the WAN Insights predictive engine. Option C is incorrect because WAN Insights explicitly uses infrastructure telemetry rather than ThousandEyes agent-based synthetic data for its SD-WAN fabric calculations. By combiningvAnalytics performance metricsandapplication flow data, WAN Insights can provide the "Predictive Path Recommendatio41ns" that are a hallmark of modern network assurance.