Free Cisco 300-445 Exam Dumps September 2026

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, capacity planning and optimization are driven by telemetry data such as NetFlow. When NetFlow identifies that delay and jitter---metrics highly impactful to real-time traffic---spike during peak hours, it indicates that high-priority packets are competing for resources with bulk data.

The most appropriate recommendation is to tune the existing QoS configuration (Option C). This involves adjusting the Queuing and Scheduling policies on the routers to ensure that voice and video traffic (typically marked with EF and AF41/AF42 DSCP values) is serviced before other traffic classes during periods of congestion. This solution is targeted, cost-effective, and directly addresses the observed jitter issues without the need for massive capital expenditure.

Reviewing other options:

Option A: A complete QoS redesign is often unnecessary and too invasive for solving peak-hour jitter if a basic QoS framework is already in place.

Option B: Increasing bandwidth on 'all' links is a 'brute force' approach that is expensive and fails to address the underlying problem of traffic prioritization.

Option D: Hardware replacement is a last resort and would not resolve delay/jitter if the new hardware still lacks a properly tuned QoS policy.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) framework, managing a large-scale deployment of Endpoint Agents requires efficient categorization through Agent Labels. Agent labels are used to group agents for targeted test assignment and data filtering based on specific attributes like location, OS, or naming conventions.

ThousandEyes supports the use of wildcards (specifically the * symbol) within label filters to allow for dynamic and scalable grouping. In this scenario, the organization has adopted a naming convention of agentname-network. To capture all agents connected to the 'corporate' network regardless of the unique agentname prefix, the engineer should use the filter *-corporate (Option A) in the hostname or agent name field. The asterisk functions as a placeholder for any string of characters, ensuring that any agent ending with '-corporate' is automatically added to the label as soon as it registers with the ThousandEyes platform.

Other options are incorrect for the following reasons:

agentname-* (Option B): This would only match agents that literally start with the string 'agentname-', which is a placeholder and not a representative filter for a group of uniquely named agents.

agent*corporate (Option C): This filter is overly broad and lacks the specific hyphen; in the organization's naming convention, potentially leading to the inclusion of unintended agents.

No wildcard (Option D): Wildcards are a documented and essential feature of the ThousandEyes label system to facilitate automated management in enterprise environments.

By implementing wildcard-based labels, the network administrator ensures that new agents are seamlessly integrated into the assurance strategy without the need for manual manual intervention.

Establishing an accurate baseline is a cornerstone of the Designing 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 to capture 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 as Cisco Catalyst Center or ThousandEyes) 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.

Under the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) guidelines, the integration between ThousandEyes and Cisco Secure Client (formerly AnyConnect) is designed to provide visibility into the hybrid workforce experience. The integration primarily leverages network performance data from the user's device (Option C) to diagnose connectivity and application issues.

This data is collected by the ThousandEyes Endpoint Agent, which is deployed as a module within the Cisco Secure Client.10 The agent captures real-time telemetry from the user's laptop or workstation, including Wi-Fi signal strength, CPU and memory utilization, and local network gateway latency. Specifically, for remote users, it monitors the health of the VPN tunnel and the performance of applications as seen from the end-user's vantage point.

When a user reports a 'slow application,' this integrated telemetry allows IT teams to determine if the root cause is the user's home Wi-Fi, a saturated VPN concentrator, or an issue within the ISP underlay. The agent performs Automated Session Testing (AST), which maps the network path as soon as a user joins a critical meeting or accesses a SaaS tool, providing a granular view of every hop between the device and the service destination. Unlike hardware configuration data (Option A) or behavioral analytics (Option B), the focus here is strictly on the network performance metrics that impact digital experience.

Therefore, the collection of device-centric network performance data is the core function of the Cisco Secure Client and ThousandEyes integration.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, monitoring modern APIs often requires handling complex authentication flows, such as OAuth 2.0 with specific client credential parameters. The provided exhibit illustrates the HTTP Authentication options available within a standard HTTP Server test: None, Basic, NTLM, Kerberos, and OAuth.

According to the ENNA implementation standards, while the HTTP Server test type supports OAuth (Option C), its native implementation is limited. Specifically, it is designed to use a pre-existing token or a simple token refresh flow; it does not support the injection of custom parameters in the initial POST request required to obtain a token in many enterprise client-credential scenarios. Basic and NTLM (Options A and B) are legacy protocols that rely on simple username/password headers and cannot facilitate the multi-step token exchange process described.

To fulfill the requirement of a two-step flow (POST for token, followed by GET for resource), engineers must use more flexible test types:

Transaction scripts (Option D): These allow the engineer to write custom JavaScript using the ThousandEyes transaction library to programmatically handle the POST request, parse the resulting JSON token, and then pass that token into the subsequent GET request's header.

API tests (Option E): These are purpose-built for API monitoring and natively support the definition of variables and multi-step requests where the output of one call (the token) serves as the input for the next.

By utilizing these advanced test types, the engineer can successfully navigate complex authentication requirements that the standard HTTP Server test cannot accommodate.

In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) curriculum, understanding the visibility gap between the SD-WAN overlay and the transport underlay is a core competency. The provided exhibit illustrates a ThousandEyes Enterprise Agent deployed on a Branch Edge Router performing tests across two distinct paths: Internet (reaching a destination at 64.100.249.90) and MPLS (reaching a destination at 172.29.0.2).

According to the ENNA architecture guidelines, ThousandEyes is primarily utilized to provide hop-by-hop visibility into the underlay network. While SD-WAN controllers like vManage provide native monitoring for the overlay---the logical IPsec tunnels (Option B) that form the SD-WAN fabric---they often lack granular visibility into the physical service provider paths (the underlay) that carry those tunnels. The exhibit specifically highlights the agent probing the transport networks (Transport VPN0) directly, bypassing the overlay tunnels to measure the raw performance of the ISP and MPLS circuits.

By monitoring the underlay (Option A), the engineer can identify if high latency or packet loss is caused by a specific hop within the service provider's infrastructure or at a peering point. This 'underlay visibility' is critical for troubleshooting SD-WAN performance issues where the overlay may report a tunnel down, but the root cause lies in a BGP routing change or physical fiber cut in the provider network. ThousandEyes Enterprise Agents, natively integrated into Catalyst 8000 and ISR 4000 platforms, allow for this persistent underlay monitoring without additional hardware.

Overlay (Option C): While ThousandEyes can monitor overlay performance, the exhibit's focus on the raw IP addresses (Internet and MPLS) in the transport VPN indicates an underlay test.

IPsec/GRE Tunnels (Options B & D): These represent the transport mechanisms of the overlay. ThousandEyes probes the path under these tunnels to ensure the transport health is sufficient to support the fabric.

In the Designing 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 as CPU utilization on 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 like Cisco Catalyst Center or 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. While DNS resolution time (Option A), HTTP response times (Option B), and Path 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.