Free 300-445 Questions Attempt

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.

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum,ThousandEyes WAN Insightsis a predictive analytics solution designed to transform network management from a reactive to a proactive model.10In 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 isanalyzing historical SD-WAN performance datato generatepath recommendations(Option B). WAN Insights integrates withCisco Catalyst SD-WAN Manager(vManage) andvAnalyticsto ingest massive volumes of telemetry.12It 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 theApplication-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 voice16probes. 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

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, transaction monitoring is essential for validating complex, multi-step user workflows. When an engineer with limited scripting experience needs to monitor a OneDrive file download process, the ThousandEyes ecosystem provides several tools to simplify the creation of robustTransaction tests.

The correct approach is to leverage all available resources, makingOption Dthe definitive answer. First, theThousandEyes Recorder IDE(Option B) is a critical tool for non-scripters. It allows an engineer to perform the actual workflow—navigating to OneDrive, logging in, and initiating the download—in a browser environment on their local machine while the tool records every click and keyboard entry. The Recorder automatically generates the corresponding JavaScript code using theThousandEyes transaction library. Second, the platform provides pre-builtscript templates(Option A), such as those forOffice 365andOneDrive, which include baseline logic and best practices for these specific services. Third, the officialtransaction-scripting-examplesrepository on GitHub (Option C) is a maintained source of code snippets. This is particularly useful for implementing advanced logic, such as aretry mechanism, which ensures that the test does not report a "failure" due to a transient network hiccup but instead attempts the action again before triggering an alert.

As shown in the provided exhibit, a typical script uses import statements from @thousandeyes and selenium-webdriver to control the browser. By combining theRecorderfor the basic flow, aTemplatefor service-specific nuances, andSample Scriptsfor logic enhancements like retries, the engineer can deploy a highly reliable assurance test without deep coding expertise. Therefore, all three actions are highly recommended and valid within the ENNA implementation framework.