Last Attempt 300-445 Questions

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In theDesigning and Implement7ing Enterpr8ise Network Assurance (300-445 ENNA)framework, configuring effective alert rules is critical for distinguishing between standard network noise and actionable application-layer failures. ForWeb - HTTP Servertests, ThousandEyes allows engineers to monitor both network-level metrics (like Connect time) and application-level indicators (like HTTP response codes).

The requirement is to trigger an alert specifically when the HTTP server responds with aserver error. In the HTTP protocol, server errors are categorized as the5XXseries of status codes (e.g., 500 Internal Server Error, 503 Service Unavailable, 504 Gateway Timeout). To meet this requirement, the engineer must configure a location alert condition where theMetricis set toResponse Codeand the condition value isserver error(5XX)(Option D).

Reviewing the other options:

Error type is any (Option A):While this would capture server errors, it would also trigger for 4XX client errors (like 404 Not Found) and network-layer timeouts, making it too broad for a specific "server error" requirement.

Wait Time is Dynamic (Option B):This monitors the time-to-first-byte using statistical baselining. While high wait times oftenprecede5XX errors, this condition only alerts on latency, not on the actual error code itself.

Response Time (Option C):Similar to wait time, this monitors performance speed rather than the logical success or failure of the server's response.

By specifically selectingResponse Code: server error(5XX), the engineer ensures that the operations team is only notified when the application backend is experiencing a functional failure, rather than just a slow response or a client-side misconfiguration.

Establishing an accurate baseline is a cornerstone of theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)methodology. A baseline must represent the "typical" behavior of the network across its standard operating cycle to be useful for anomaly detection and capacity planning. When monitoring interface utilization on a critical link, it is vital tocapture both peak and off-peak traffic patterns(Option C).

Most enterprise networks exhibit cyclical traffic patterns, often referred to as "diurnal" cycles, where utilization spikes during business hours (peak) and drops significantly at night or on weekends (off-peak). If an engineer only collects data during the lowest volume period (Option A), the baseline will be unrealistically low, leading to "false positive" alerts when normal business traffic returns. Conversely, only capturing peak data might hide background synchronization tasks or backup windows that occur during off-hours.

By selecting a representative time period—typically one full week or a month—the engineer ensures the baseline accounts for variations such as Monday morning "logon storms," mid-week steady states, and weekend maintenance. This comprehensive view allows the assurance platform (such asCisco Catalyst CenterorThousandEyes) to calculate a standard deviation and establish dynamic thresholds. If utilization deviates from these historically representative norms, the system can trigger an alert based on a true anomaly rather than a predictable daily surge. Aligning with a financial year (Option B) is irrelevant to technical performance, and limiting the data (Option D) compromises the baseline's statistical validity.

InDesigning and Implementing Enterprise Network Assurance (300-445 ENNA), dashboard widgets must be mapped to the correct telemetry data source and statistical measure. When an agent is installed as aLinux packageon an organization's infrastructure, it is classified as anEnterprise Agent.

The correct configuration for this widget isCloud & Enterprise Agents and Mean(Option B).

Data Source:Since the Linux-based agent is an Enterprise Agent, it shares the same data source category as Cloud Agents in the ThousandEyes dashboard configuration menu.

Measure:To display the "average" packet loss as requested, theMean(the arithmetic average) is the appropriate statistical measure.

Other options are unsuitable:

Option A:Endpoint Agentsare those installed on Windows/macOS user devices, not typically a standard Linux server package used for infrastructure monitoring.Medianwould show the middle value, not the average.

Option C:Routingrefers to BGP data, which does not report packet loss in the same way as synthetic network tests.

Option D:Devicesrefers to hardware monitored via SNMP, and annth Percentileis a specific statistical cutoff (like 95th percentile) rather than a simple average.