300-445 Questions and Answers

For theDesigning 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 aMicrosoft Domain Controllerto manage its internal domain, the standard mechanism for automated software distribution isGroup 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.

WhileMicrosoft Intune (Option A)is a modern cloud-based endpoint management solution, the question specifically references aMicrosoft 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.8PowerShell (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 Objectsis the verified deployment method for this scenario.

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, selecting the appropriate test type is essential for isolating performance bottlenecks in complex web applications. When users report issues with specific multi-step workflows—such as logging into a portal or interacting with a shopping cart—theTransaction test type(Option C) is the most effective tool.

A Transaction test is a specialized Web-layer test that utilizes a script (typically written in JavaScript/TypeScript) to mimic real user interactions with a website. Unlike a simpleHTTP Server test(Option A) that only checks for a 200 OK response from a single URL, or aPage Load test(Option B) that measures the rendering of a single page, a Transaction test follows a predefined user journey. For this retail scenario, the test can be configured to navigate to the homepage, enter credentials, click the login button, search for a product, and add it to the cart.

The primary advantage of this approach is that it provides granular, step-specific timing. It allows the engineer to identify precisely where the latency or failure occurs—for example, if the backend database takes too long to process the login or if an API call fails during the "add to cart" action. WhileAgent-to-server(Option D) andDNS Server(Option E) tests provide valuable network and infrastructure data, they cannot validate the functional logic of a web application.Agent-to-agent(Option F) is used for measuring throughput between two managed points and is irrelevant for public-facing website testing. Therefore, for troubleshooting interactive web processes, theTransaction test typeis the definitive choice for pinpointing the source of the issue.

In theDesigning 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, theEndpoint Agentis the required vantage point.

The correct combination isReal 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 utilizingReal 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.

In theDesigning 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 asCPU utilizationon 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 likeCisco Catalyst Centeror 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. WhileDNS resolution time(Option A),HTTP response times(Option B), andPath 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.

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)framework emphasizes that the goal of internet intelligence is to enable rapid and accurate escalation to the party responsible for a service degradation. Based on the evidence of a BGP Hijack identified in the previous question, the issue is occurring entirely within the public internet ecosystem.

The most appropriate next step is toreach out to the Internet Service Provider (ISP) to report the suspected BGP hijacking incident(Option B). Since the traffic is being misdirected by an external Autonomous System (AS 10297) before it reaches the intended destination (AS 16509), the fix must occur at the routing policy level of the major transit providers. The network administrator should provide the ISP with the ThousandEyes "Share Link" or screenshots showing the path change and the unauthorized AS announcement, as this data serves as proof to accelerate the ISP's mitigation efforts, such as implementing prefix filters or contacting the offending network.

Other options are ineffective for this specific scenario:

Option A:The path visualization shows that traffic is successfully leaving the local network and reaching the public internet; the problem is many hops away from the internal routers.

Option C:Blocking trafficfromAS 10297 does not solve the problem of your traffic beingattractedto it. The hijack affects how the rest of the world (including your ISP) sees the route to your destination.

Option D:DNS is not the issue; the agent successfully resolved the hostname to the correct IP, but the BGP layer misdirected the packets at the routing level.

By identifying the issue as an external routing event, the administrator avoids wasting internal resources and directly triggers the necessary external remediation.

In the context ofDesigning and Implementing Enterprise Network Assurance (300-445 ENNA), capacity planning requires accurate baselining of interface bandwidth against its theoretical and provisioned limits. AnalyzingExhibit 4.5 Question 4(image_79d4fc.jpg) reveals a significant discrepancy between the physical reality of the link and its configuration within the monitoring tool.

The exhibit displays a capacity planning dashboard with a calendar heatmap and traffic graphs. The heatmap for February through May shows a high frequency of "Severe" (red) utilization blocks. Looking at theEgressgraph forTue May 07 2024, the traffic spikes clearly exceed 40.0 Mbps. Crucially, the dashboard indicates an "Egress Capacity" of49.5 Mbpsand reports that the "Highest consumption" was 48.0 Mbps, representing97%of the available bandwidth.

However, the question states that the ISP provides a1 Gbps (1000 Mbps) connection. Since the actual traffic being sent is less than 50 Mbps, the link is nowhere near physical saturation. The "Severe" alerts and high utilization percentages are occurring only because the monitoring software (likely ThousandEyes or a similar NMS) is configured with aMaximum Capacityof only 49.5 Mbps for this interface. This misconfiguration causes the tool to calculate utilization based on a much smaller "pipe" than what actually exists, leading to false-positive alerts.

Therefore, the most likely action to fix this observed behavior is toreconfigure maximum capacity for the interface(Option B) to match the 1 Gbps specification.

Option Ais unnecessary because the current link is only being utilized at ~5% of its 1 Gbps potential.

Option Cis a restrictive policy change that is not justified given the actual available headroom.

Option Dmight shift how data is displayed but will not fix the underlying mathematical error in utilization calculations.

According to theDesigning 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,18the goal is to determine if the issue is global, regional, or specific to certain ISP paths leading to the data center.

TheCloud 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.19Because they arepre-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 Agentsprovide the necessary breadth and immediate availability to perform rapid root cause analysis for widespread application performance issues.

AppDynamics

Catalyst Center

ThousandEyes

Meraki

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)architecture, each platform is positioned to address a distinct domain of visibility and management within the modern IT visibility framework.

AppDynamics(matched to Case 1) is specifically engineered for Full-Stack Observability and Application Performance Monitoring (APM). Its unique focus is linking technical application performance—such as code-level execution and database queries—directly to business outcomes and real-time business metrics. By doing so, it allows organizations to pinpoint how application latency impacts revenue or user satisfaction, making it the primary choice for business impact analysis.

Catalyst Center, formerly DNA Center (matched to Case 2), serves as the foundational controller for campus and enterprise network infrastructure. It leverages AI-driven insights to automate network operations, enforce security policies, and provide at-a-glance health monitoring for local devices and clients. It is the definitive management system for connecting and securing "inside" the corporate network perimeter.

ThousandEyes(matched to Case 3) provides "Internet Intelligence" and is the key solution for monitoring the service delivery chain across environments the enterprise does not own. By utilizing a global network of Cloud, Enterprise, and Endpoint agents, it provides end-to-end visibility from any user location to any application (SaaS or Cloud) over any network (Internet, ISP, or WAN).

Meraki(matched to Case 4) is a cloud-first platform designed for managing highly distributed network environments and IoT deployments. It provides end-to-end visibility through a simplified dashboard that integrates wireless health, switching, and security across thousands of sites, making it ideal for lean IT teams managing distributed retail or branch locations.

Each platform utilizes different data collection methods—active synthetic testing for ThousandEyes and passive monitoring/AI telemetry for Catalyst Center—to ensure comprehensive network assurance across the entire enterprise ecosystem.

In the context ofDesigning and Implementing Enterprise Network Assurance (300-445 ENNA), analyzing page-load metrics within Cisco ThousandEyes requires identifying the primary bottlenecks that contribute to theTotal Page Load Time. The provided screenshot displays a "Page Breakdown" of 7 resources totaling 953 kB. A critical observation of the pie chart reveals thatImages(the teal-colored segment) constitute the vast majority of the page's payload and resource count.

When the goal is to reduce the page-load time from1023 msto below500 ms, the engineer must target the heaviest components.Lazy loadingis a design pattern that defers the initialization of non-critical resources at page load time. Instead of loading all images simultaneously when the userfirst navigates to the URL, lazy loading ensures that images are only downloaded as they are about to enter the viewport. This significantly reduces the initial DOM load time and the total Page Load Time because the browser does not have to wait for large image files to be fully retrieved before declaring the page "loaded."

Alternative options are less effective in this specific scenario based on the data:

AJAX (XHR/Fetch):The chart shows that XHR and Fetch resources represent a negligible sliver of the total weight; optimizing them would yield minimal gains.

Moving IMG elements:While moving scripts to the bottom can help with rendering, moving image elements to the bottom of the body does not stop the browser from initiating the download requests immediately, thus failing to significantly reduce the total load time.

CDN for Fonts:The "Font" category is also a small fraction of the total 953 kB. While a CDN is a best practice for latency, it does not address the primary "weight" issue caused by the images.

Therefore, implementinglazy loading(Option C) is the most impactful recommendation. It directly addresses the largest resource consumer (Images) identified in the ThousandEyes Page Breakdown, allowing the engineer to reach the sub-500 ms performance target.

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.

Detailed Explanation:

The IT Operations Dashboard includes a section at the beginning that explicitly displays active alert rules and their status. According to the dashboard configuration, the reason for the current alert trigger is Network packet loss. This indicates that the underlying network path for the application is experiencing packet drops exceeding the defined threshold, even if the application layer remains partially functional.

In the context ofDesigning 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.

TheEndpoint 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)andScheduled Synthetic Tests.24RUM 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.25Simultaneously, 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 deployingEndpoint Agents, the architect ensures they have granular, contextual data that correlates application performance directly with the user’s device and local network environment.