Cisco 300-445 Exam Dumps

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.

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.

Within the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) framework, the transition from 'polling' to 'streaming' is a major architectural shift. To populate real-time dashboards in external observability platforms like Grafana, Splunk, or AppDynamics, the architect should utilize the OpenTelemetry (OTel) integration (Option B).

ThousandEyes for OpenTelemetry is a push-based API built on the standardized OpenTelemetry Protocol (OTLP). Unlike traditional REST API polling (Option A), which retrieves data at fixed intervals and can be subject to rate limiting and latency, the OTel integration allows ThousandEyes to stream granular network metrics as they are collected. These metrics---including latency, loss, jitter, and HTTP response times---are exported in a standardized format that is natively understood by modern observability backends. This allows the platform to populate complex visualizations such as time-series graphs, heatmaps, and multi-metric tables in near real-time, providing a 'single pane of glass' view that correlates network performance with application and infrastructure telemetry.

A key advantage of the OTel approach is data portability and correlation. By applying metadata tags to ThousandEyes tests, the data can be filtered and categorized within the external dashboard to match the organization's business logic (e.g., grouping by region or application tier). This enables SREs and NetOps teams to quickly identify if a performance dip in an application dashboard correlates with a spike in internet latency measured by ThousandEyes. Options C and D do not provide the streaming data pipeline required for real-time external dashboard population. Thus, OpenTelemetry is the definitive choice for high-fidelity, real-time observability integration.

In the Designing 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 robust Transaction tests.

The correct approach is to leverage all available resources, making Option D the definitive answer. First, the ThousandEyes 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 the ThousandEyes transaction library. Second, the platform provides pre-built script templates (Option A), such as those for Office 365 and OneDrive, which include baseline logic and best practices for these specific services. Third, the official transaction-scripting-examples repository on GitHub (Option C) is a maintained source of code snippets. This is particularly useful for implementing advanced logic, such as a retry 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 the Recorder for the basic flow, a Template for service-specific nuances, and Sample Scripts for 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.

In the context of Designing and Implementing Enterprise Network Assurance (300-445 ENNA), capacity planning requires accurate baselining of interface bandwidth against its theoretical and provisioned limits. Analyzing Exhibit 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 the Egress graph for Tue May 07 2024, the traffic spikes clearly exceed 40.0 Mbps. Crucially, the dashboard indicates an 'Egress Capacity' of 49.5 Mbps and reports that the 'Highest consumption' was 48.0 Mbps, representing 97% of the available bandwidth.

However, the question states that the ISP provides a 1 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 a Maximum Capacity of 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 to reconfigure maximum capacity for the interface (Option B) to match the 1 Gbps specification.

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

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

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