300-445 Exam dumps PDF | Exam-Dumps.net (Page: 2)

QUESTION: 9

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?

Synthetic Agent
Enterprise Agent
Endpoint Agent
Cloud Agent

Answer(s): D

Explanation:

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.

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QUESTION: 10

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?

Synthetic Agent
Enterprise Agent
Cloud Agent
Endpoint Agent

Answer(s): D

Explanation:

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.

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QUESTION: 11

A network engineer wants to measure their SD-WAN performance metrics.
Which agent deployment method is most suitable for this scenario?

Install an agent on the overlay network
Install an agent on the DMZ
Install an agent on their LAN
Install an agent on the underlay network

Answer(s): D

Explanation:

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.

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QUESTION: 12

A network engineer needs to monitor the performance of a business-critical web application accessed by remote employees connecting through a Cisco AnyConnect VPN.
Which two agent deployment methods are most suitable for this scenario? (Choose two)

Deploy ThousandEyes Cloud Agents in the same geographical regions as the remote employees.
Integrate ThousandEyes with Cisco AppDynamics to monitor application performance from the server-side.
Deploy ThousandEyes Enterprise Agents on the VPN concentrator where the AnyConnect clients terminate.
Utilize the ThousandEyes Endpoint Agent and deploy it on a subset of remote employee machines running Cisco AnyConnect.
Configure ThousandEyes tests from Enterprise Agents located in the data center where the web application is hosted.

Answer(s): A,D

Explanation:

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.

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QUESTION: 13

Which of the following is an example of active monitoring in network performance management?

Analyzing SNMP data to observe interface utilization on a router
Capturing packets on a network segment to identify the top talkers
Sending a continuous ping from one office to another to measure latency
Collecting NetFlow records to analyze traffic patterns over time

Answer(s): C

Explanation:

Within the framework of Designing and Implementing Enterprise Network Assurance (300-445 ENNA), network monitoring is categorized into two primary methodologies: active and passive monitoring.1 Active monitoring (Option C) is characterized by the generation of synthetic or "probes" traffic specifically designed to measure network performance.2 These probes simulate real-world user activity, such as HTTP requests, DNS queries, or ICMP pings, to baseline performance metrics like latency, jitter, and packet loss.

The core benefit of the active approach is its independence from actual user traffic. By sending a continuous ping or synthetic HTTP probe, an engineer can verify path availability and performance even during off-peak hours when no real users are on the network. In the context of Cisco ThousandEyes--a central platform in the ENNA certification--this is the primary mode of operation for Cloud, Enterprise, and Endpoint agents. For instance, a ThousandEyes network test proactively sends packets to a target IP or URL to visualize the hop-by-hop underlay and overlay paths.

Conversely, options A, B, and D represent passive monitoring techniques. Passive monitoring involves observing and analyzing traffic that is already traversing the network.3 Methods such as SNMP (Option A) provide device-level health data like CPU load and interface utilization, while packet captures (Option B) and NetFlow (Option D) analyze the characteristics of existing user flows to determine top talkers or traffic patterns.
While passive monitoring is excellent for volume and utilization analysis, it lacks the proactive capability to test a path's performance before a user encounters a failure. Therefore, sending a synthetic probe like a continuous ping is the definitive example of active monitoring.

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QUESTION: 14

What is a primary advantage of passive monitoring over active monitoring?

Passive monitoring can measure the network's performance under synthetic conditions.
Passive monitoring can provide real-time data on network performance without adding traffic to the network.4
Passive monitoring allows for the generation of test traffic to simulate user behavior.
Passive monitoring can directly measure the performance of specific network services or protocols.

Answer(s): B

Explanation:

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) architecture, a critical design consideration is the impact of the monitoring solution on the production environment. The primary advantage of passive monitoring (Option B) is its non-intrusive nature; it provides insights into network performance and traffic composition without injecting additional "synthetic" overhead into the data plane.

Passive techniques--such as Cisco Meraki Insight (MI), NetFlow, and SNMP--rely on the telemetry generated by existing user traffic or the device's own control plane. For example, Meraki Insight analyzes HTTP/S flows as they naturally pass through a Meraki MX appliance to derive application performance scores, rather than sending separate probes.5 This ensures that the monitoring tool itself does not consume bandwidth or contribute to network congestion, which is particularly vital in bandwidth-constrained branch environments or on high-utilization links.

In contrast, active monitoring (Options A and C) requires the deliberate generation of synthetic traffic, which can potentially skew results if the volume is too high or if the network is already at capacity.
While active monitoring is essential for proactive troubleshooting (measuring performance before users complain), passive monitoring is the preferred method for long-term historical analysis of real user experience and infrastructure utilization because it captures what is actually happening on the wire. Option D is a shared capability; both types can measure specific services, but only passive monitoring does so while remaining transparent to the network load. Therefore, the lack of added traffic is the definitive advantage of the passive approach.

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QUESTION: 15

A network administrator observes a recurring pattern in their Cisco SD-WAN: during peak business hours, users at a specific branch office experience poor voice call quality, characterized by choppy audio and delays.6 The administrator suspects that network congestion is contributing to this issue and wants to leverage ThousandEyes WAN Insights to improve the situation proactively.
Which capability of WAN Insights is most relevant to this scenario?

Monitoring the public internet paths to the voice call service provider and identifying outages or performance bottlenecks.7
Analyzing historical SD-WAN performance data during peak hours and recommending alternative paths that prioritize voice traffic based on its SLA.
Generating synthetic voice traffic to proactively test network paths and identify potential congestion points during peak hours.8
A set of network management tools that leverage SNMP and flow protocols into a single dashboard.
Providing real-time alerts on security threats targeting voice traffic within the SD-WAN.9

Answer(s): B

Explanation:

According to the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, ThousandEyes WAN Insights is a predictive analytics solution designed to transform network management from a reactive to a proactive model.10 In 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 is analyzing historical SD-WAN performance data to generate path recommendations (Option B). WAN Insights integrates with Cisco Catalyst SD-WAN Manager (vManage) and vAnalytics to ingest massive volumes of telemetry.12 It 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 the Application-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 voice16 probes. 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

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QUESTION: 16

Thousand23Eyes WAN Insights integrates with Cisco SD-WAN to provide visibility into network performance and generate path recommendations.
Which two data sources from the SD-WAN

environment are e25ssential for WAN Insights to function? (Choose two)

Configuration data from vManage, su27ch as device templates and centralized policy settings.28
Historical network performance metrics collected by vAnalytics, including loss, latency, and jitter for SD-WAN tunnels.
Data collected from ThousandEyes endpoint agents installed on end-user devices within the SD- WAN.
Application traffic flow data from the SD-WAN data plane, categorized by vManage application lists.
Syslog messages from SD-WAN edge routers, indicating device events and errors related to tunnel establishment.

Answer(s): B,D

Explanation:

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 Recommendatio41ns" that are a hallmark of modern network assurance.

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Cisco 300-445 Exam (page: 2) Cisco Designing and Implementing Enterprise Network Assurance Updated on: 17-Feb-2026

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Cisco 300-445 Exam (page: 2)
Cisco Designing and Implementing Enterprise Network Assurance
Updated on: 17-Feb-2026