Cisco 300-445 Exam With Confidence Using Practice Dumps

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 theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, identifying routing anomalies such as BGP Hijacking is a critical aspect of external network assurance. A BGP Hijack occurs when an Autonomous System (AS) illegitimately announces a prefix it does not own, causing traffic intended for the rightful owner to be misdirected.

By contrastingExhibit 3.4-1(Before the Outage) andExhibit 3.4-2(During the Outage), a definitive change in the routing path is observed. In the baseline view (Exhibit 3.4-1), traffic from the Los Angeles agent reaches the destination network 54.239.104.0/23, which is correctly associated withAmazon.com, Inc. (AS 16509). However, during the outage (Exhibit 3.4-2), the path visualization shows the traffic being redirected into a different network:eNET Inc. (AS 10297). At this new location, the traffic encounters100% Forwarding Lossat the node ten7-8.core-2.xlhost.com.

Thechange in the Autonomous System (AS) numberfrom 16509 to 10297 (Option B) is the most concrete indicator of a BGP Hijack. While symptoms such asAvailability Drop(Option A),Response Delay(Option C), andPacket Loss(Option D) are clearly visible in the ThousandEyes telemetry, these symptoms are generic and could be caused by various other issues like physical link failures or congestion. The shift in the AS path—specifically to an AS that does not legitimately host the target's IP prefix—provides the forensic evidence needed to identify the root cause as a routing takeover. This visualization allows network administrators to bypass internal troubleshooting and immediately focus on external remediation with upstream providers.

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, the evolution of network monitoring includes moving from periodic polling to real-time data streaming. The exhibit displays a curl command targeting the ThousandEyes API v7 /stream endpoint. When integrating ThousandEyes with high-performance observability platforms likeGrafana, the standardized and recommended method for machine-to-machine data exchange is throughOpenTelemetry (OTel).

According to the ENNA architecture guidelines, the ThousandEyesStreaming APIallows users to push granular test metrics (such as network latency, packet loss, and jitter) to external collectors in an OTel-compatible format. In the provided JSON payload, the "type" field is a mandatory parameter that defines the integration protocol. For Grafana, which natively supports OpenTelemetry Protocol (OTLP) via its OpenTelemetry Collector, the value must be set to"opentelemetry"(Option A). This tells the ThousandEyes streaming engine to encapsulate the data according to the OTel semantic conventions, ensuring that Grafana can correctly interpret and visualize the metrics without additional custom parsing logic.

While other options exist in the ThousandEyes ecosystem, they do not fit the specific API call shown for this use case:

Custom Webhooks(Option B) are typically used for event-driven alerts and notifications (e.g., sending a POST request when a threshold is breached) rather than continuous high-fidelity metric streaming.

Push-apiandpoll-api(Options C and D) are not valid "type" values within the context of the v7 /stream endpoint, as the streaming service specifically utilizes the OpenTelemetry framework for real-time delivery.

By selectingopentelemetry, the network engineer enables a robust "push-based" integration that provides real-time visibility into application performance and network health, leveraging Grafana's advanced dashboarding capabilities to analyze ThousandEyes telemetry data alongside other enterprise infrastructure metrics.

Introduction to ThousandEyes for OpenTelemetry

This video provides a foundational understanding of how ThousandEyes uses modern streaming frameworks to export critical performance data to external observability platforms.