You're analyzing NetFlow data for a network supporting voice and video traffic. The data shows consistent spikes in delay and jitter during peak hours. Which optimization would you recommend?
Answer : C
In the Designing 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 to tune 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.
What Meraki platform supports ThousandEyes?
Answer : A
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.
Which deployment option should a network administrator use to deploy the ThousandEyes Endpoint Agent to all users on their internal domain using a Microsoft Domain Controller?
Answer : B
For the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) exam, understanding enterprise-scale deployment of the Endpoint Agent (EPA) is vital for ensuring comprehensive user-centric visibility. When an organization utilizes a Microsoft Domain Controller to manage its internal domain, the standard mechanism for automated software distribution is Group Policy Objects (GPOs) (Option B).
Deploying the ThousandEyes Endpoint Agent via GPO allows a network administrator to push the .msi installer package to all Windows-based workstations joined to the domain. This method is highly effective for large-scale environments because it ensures that the agent is installed automatically upon machine startup or user login, without requiring manual intervention from the end-user or the IT staff at each individual machine. The GPO can be configured to perform a 'silent installation,' which runs in the background, ensuring a seamless experience for the employees while providing the IT team with the necessary performance metrics.
While Microsoft Intune (Option A) is a modern cloud-based endpoint management solution, the question specifically references a Microsoft Domain Controller, making GPO the most direct and traditional choice for that specific infrastructure. JAMF (Option C) is specifically for Apple (macOS/iOS) device management and would not be used for a Windows domain deployment.8 PowerShell (Option D) can be used for scripting, but it lacks the centralized policy enforcement and automatic compliance checking provided by GPOs in an Active Directory environment. Therefore, Group Policy Objects is the verified deployment method for this scenario.
A network engineer is investigating widespread reports of poor performance for a data center-hosted web application. Which ThousandEyes agent type would be most effective for quickly identifying the root cause?
Answer : D
According to the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) guidelines, troubleshooting widespread performance issues for a public or data center-hosted app17lication requires an 'outside-in' perspective. When reports are widespread,18 the goal is to determine if the issue is global, regional, or specific to certain ISP paths leading to the data center.
The Cloud Agent (Option D) is the most effective tool for this task because these agents are maintained by Cisco ThousandEyes in over 240+ locations worldwide within Tier 1, 2, and 3 ISPs and cloud provider regions.19 Because they are pre-deployed and immediately available, a network engineer can instantly run tests from multiple global locations toward the data center-hosted application without having to install any software or manage any infrastructure. This allows the engineer to quickly compare performance metrics (latency, loss, and page load times) across different geographies. If Cloud Agents in London report no issues while those in New York report high packet loss, the engineer can immediately pinpoint the root cause as a regional ISP or peering issue rather than a failure within the data center itself.
Enterprise Agent (Option B): While these could be used if they were already installed in various branch offices, they require ownership of the infrastructure and deployment time. They are better suited for 'inside-out' monitoring.
Endpoint Agent (Option C): These are useful for troubleshooting individual user experience but are not the 'quickest' way to baseline global performance against a data center application during a widespread event.
Synthetic Agent (Option A): As noted previously, this is a generic term describing the underlying technology used by all ThousandEyes agent types.
Therefore, Cloud Agents provide the necessary breadth and immediate availability to perform rapid root cause analysis for widespread application performance issues.
Which type of test are we using for these dashboards (Executive and IT Operations)?
Answer : B
According to the Designing 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 as Page 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.
Simple HTTP Server tests (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 the Page Load test type.
An engineer needs to create a test that requires authentication configuration to monitor an API. The test must send a POST request with client credentials parameters to get a token. The token then needs to be sent out on a GET request to be authorized to get the resource.

What must be done to meet the requirements? (Choose two)
Answer : D, E
In the Designing 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 the HTTP Authentication options 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 supports OAuth (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.
An architect needs to measure end-user experience for internal web applications and SaaS products.20 Which ThousandEyes agent should be deployed for this purpose?
Answer : D
In the context of Designing and Implementing Enterprise Network Assurance (300-445 ENNA), measuring the 'lived experience' of an end-user requires data collection from the actual device being used to access the services. Unlike server-side or infrastructure-side monitoring, user experience (UX) monitoring must account for local variables like Wi-Fi signal quality, CPU/memory usage, and browser-level pe21rformance.
The Endpoint Agent (Option D) is the correct choice for this architecture. It is a lightweight software service installed directly on Windows or macOS workstations, as well as RoomOS devices. The Endpoint Agent provides a dual-monitoring approach: Real User Monitoring (RUM) and Scheduled Synthetic Tests.24 RUM captures actual browser sessions to SaaS (e.g., Salesforce, Microsoft 365) or internal apps, providing a 'Experience Score' and a detailed waterfall view of page load components.25 Simultaneously, the agent can run background synthetic network tests to measure latency and path visualization from the user's specific location, whether they are in a branch office, at home on a VPN, or in a coffee shop.
Comparing other agents:
Enterprise Agents (Option B) can simulate a user at a branch office, but they cannot provide insight into the specific health of an individual's laptop or their unique Wi-Fi environment.
Cloud Agents (Option C) are entirely outside the user's network and cannot measure the performance of internal web applications or the 'last mile' connectivity of the employee.
Synthetic Agent (Option A) remains a distractor term.
By deploying Endpoint Agents, the architect ensures they have granular, contextual data that correlates application performance directly with the user's device and local network environment.