Cisco Designing and Implementing Enterprise Network Assurance (300-445 ENNA) 300-445 Exam Dumps: Updated Questions & Answers (October 2026)

Troubleshooting intermittent performance issues is a core component of the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum. When a transaction test using Basic Authentication fails randomly, the engineer must employ a multi-layered diagnostic approach to determine if the fault lies with the credentials, the network path, or the application server.

The first step is to verify the correctness of credentials (Option B). Because Basic Authentication encodes credentials in the header, any change in user permissions or account lockouts will trigger immediate failures. Performing manual logins from various geographical locations helps rule out location-based access control lists (ACLs) or regional IdP sync issues.

The most data-rich diagnostic step is to analyze the ThousandEyes waterfall charts and HTTP response codes (Option C). The waterfall view allows the engineer to see if the failure happens during the initial 401 Unauthorized challenge or after the credentials are sent. If the charts show high " Wait Time " or 5xx errors, the issue is likely server-side latency or instability. If the " Connect Time " is high, the problem may be network-layer congestion.

Finally, if the telemetry indicates that the network path is healthy but the server is intermittently returning errors or timing out, the engineer should contact the web application vendor (Option D). Providing the vendor with the specific ThousandEyes " Share Link " allows them to see the exact same packet-level and browser-level data, proving that the issue is not with the client ' s network but with the server-side infrastructure. Disabling authentication (Option A) is not a valid troubleshooting step for a test designed specifically to monitor an authenticated workflow.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, understanding the distinction between different monitoring methodologies is fundamental to architecting an effective assurance strategy. When an engineer deploys a ThousandEyes Enterprise Agent on a Meraki MX appliance, they are implementing active monitoring .

Active monitoring, as defined in standard network assurance frameworks like RFC 7799, involves the generation of synthetic traffic or " probes " that are sent across the network to a specific destination. These probes, which can utilize protocols such as ICMP, TCP, or HTTP/S, simulate real user transactions to measure performance metrics including latency, packet loss, jitter, and path visualization. The Enterprise Agent acts as a dedicated vantage point, executing these tests at scheduled intervals to provide a proactive baseline of network and application health. This allows the engineer to identify performance degradation or outages even when no real users are actively using the application, ensuring that issues are detected before they impact the business.

It is important to contrast this with passive monitoring (Option B). In the Meraki ecosystem, Meraki Insight (MI) natively performs passive monitoring by observing and analyzing actual user traffic flows (HTTP/S data) as they traverse the MX appliance without injecting additional traffic. While passive monitoring is excellent for understanding real-world user experience and server response times, it relies on existing traffic and cannot provide hop-by-hop path visualization across the Internet in the same way active synthetic probing does.

By integrating the ThousandEyes Enterprise Agent—which runs as a containerized service within the MX architecture—the engineer gains the benefits of active monitoring directly from the branch edge. This eliminates the need for separate hardware and provides deep, " outside-in " and " inside-out " visibility into SaaS application performance. Therefore, the deployment of a ThousandEyes agent explicitly enables active monitoring (Option A) to supplement the native passive capabilities of the Meraki platform.

In the context of Designing and Implementing Enterprise Network Assurance (300-445 ENNA) , understanding the visibility gap in SD-WAN environments is essential. While SD-WAN controllers provide native visibility into the overlay network (the logical IPsec tunnels and fabric health), they often lack granular insight into the physical transport or underlay network provided by ISPs or MPLS circuits.

According to the ENNA architecture guidelines, the most suitable method for measuring true SD-WAN performance is to install an agent on the underlay network (Option D). By deploying ThousandEyes Enterprise Agents directly on the transport-facing interfaces (Transport VPN0 in Cisco SD-WAN terminology), engineers can perform hop-by-hop path visualization and measure metrics like packet loss, latency, and jitter across the actual provider path. This is critical because performance degradation in the overlay is almost always a symptom of an issue in the underlay, such as BGP routing instabilities or physical link congestion at an ISP peering point.

Deploying agents on the overlay (Option A) only measures the performance of the tunnel itself, which may hide specific hop-level failures occurring in the public internet. Installing an agent on the LAN (Option C) or DMZ (Option B) adds local network noise to the metrics, making it harder to isolate if a problem exists within the corporate office or the service provider network. By focusing on the underlay , the engineer ensures they have the " internet intelligence " required to hold service providers accountable to SLAs and quickly resolve connectivity issues that impact the SD-WAN fabric.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, monitoring the digital experience of internal employees requires leveraging ThousandEyes Endpoint Agents to simulate or record traffic from the user ' s specific vantage point. When an engineer needs to monitor a specific, potentially internal or non-standard web application, they must utilize the Custom Application workflow within the Endpoint Experience settings.

According to the ENNA implementation standards, the first necessary action is to Create a new custom application monitor (Option A). In the ThousandEyes portal under Endpoint Experience > Test Settings > Synthetic Tests , the " Monitor Application " button provides access to pre-defined templates for popular services like Microsoft 365 or Webex. However, for a unique enterprise application, the engineer must select " Custom Application " to define the application ' s identity, including its name and the relevant domain or URL.

The second required action is to Add a new scheduled test to the monitor (Option C). Endpoint Agents perform Scheduled Tests at regular, predefined intervals—such as every 5 or 10 minutes—to proactively check the application ' s availability, response time, and network path health without requiring user interaction. By adding a scheduled HTTP Server or Network test to the custom monitor, the engineer ensures a consistent baseline of the application ' s performance as seen from the employee ' s machine.

Reviewing the incorrect options:

Google Suite monitor (Option B): This is a specific template for Google Workspace and is not suitable for a custom application.

Dynamic Tests (Option D): These are specifically designed for collaboration tools like Zoom or Microsoft Teams to capture ad-hoc sessions; they are generally not used for baselining a standard custom web application.

Test Template (Option E): While all monitors are based on templates, " adding a new template " is not a configuration action but rather a result of the monitoring setup.

The architecture for Designing 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 is historical 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. 34 This data includes granular metrics for every SD-WAN tunnel, such as packet loss, latency, and jitter. 35 WAN 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 is application 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. 38 This information is ingested as flow records from the SD-WAN data plane and categorized based on the Application Lists defined in Cisco 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 combining vAnalytics performance metrics and application flow data , WAN Insights can provide the " Predictive Path Recommendatio 41 ns " that are a hallmark of modern network assurance.