Cisco Designing and Implementing Enterprise Network Assurance(300-445 ENNA) 300-445 Exam Dumps: Updated Questions & Answers (January 2026)

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)framework, a common architectural hurdle is monitoring applications protected by Multi-Factor Authentication (MFA). ThousandEyes transaction tests utilize automated browser sessions to simulate user behavior, but they face inherent limitations when interacting with "out-of-band" security mechanisms.

Specifically, ThousandEyes agents cannot natively interact with external hardware tokens, biometric prompts, or mobile-app-based OTP generators (Option A). Since an OTP is dynamic and time-sensitive, manually entering it into a static test configuration (Option B) is impossible for an automated, recurring test. While some complex workhooks (Option C) might theoretically interface with a virtual MFA service, this introduces significant security risks and architectural complexity that is generally discouraged in a standard assurance design.

The verified and most practical approach is toexclude the MFA step from the transaction test and focus only on the SAML login(Option D). For monitoring purposes, IT teams often create a "synthetic user" account within the Identity Provider (IdP) that is specifically exempted from MFA policies when originating from known ThousandEyes Enterprise Agent IP addresses. This allows the transaction script to validate the availability and performance of the SAML-based SSO redirect, the credential challenge, and the final application landing page. This strategy ensures that the network and application health can be baselined without the test being blocked by a security gate it was never intended to pass.

In the context ofDesigning and Implementing Enterprise Network Assurance (300-445 ENNA), analyzing page-load metrics within Cisco ThousandEyes requires identifying the primary bottlenecks that contribute to theTotal Page Load Time. The provided screenshot displays a "Page Breakdown" of 7 resources totaling 953 kB. A critical observation of the pie chart reveals thatImages(the teal-colored segment) constitute the vast majority of the page's payload and resource count.

When the goal is to reduce the page-load time from1023 msto below500 ms, the engineer must target the heaviest components.Lazy loadingis a design pattern that defers the initialization of non-critical resources at page load time. Instead of loading all images simultaneously when the userfirst navigates to the URL, lazy loading ensures that images are only downloaded as they are about to enter the viewport. This significantly reduces the initial DOM load time and the total Page Load Time because the browser does not have to wait for large image files to be fully retrieved before declaring the page "loaded."

Alternative options are less effective in this specific scenario based on the data:

AJAX (XHR/Fetch):The chart shows that XHR and Fetch resources represent a negligible sliver of the total weight; optimizing them would yield minimal gains.

Moving IMG elements:While moving scripts to the bottom can help with rendering, moving image elements to the bottom of the body does not stop the browser from initiating the download requests immediately, thus failing to significantly reduce the total load time.

CDN for Fonts:The "Font" category is also a small fraction of the total 953 kB. While a CDN is a best practice for latency, it does not address the primary "weight" issue caused by the images.

Therefore, implementinglazy loading(Option C) is the most impactful recommendation. It directly addresses the largest resource consumer (Images) identified in the ThousandEyes Page Breakdown, allowing the engineer to reach the sub-500 ms performance target.

Detailed Explanation:

This analysis requires extracting two specific browser-rendering metrics from the IT Operations Dashboard. By placing the cursor over the latest metrics data points:

Page Load time:1479.6 ms.

DOM load time: 1354.3 ms.

By subtracting the DOM time from the Page Load time ($1479.6\text{ ms} - 1354.3\text{ ms} = 125.3\text{ ms}$), the engineer identifies the duration spent rendering the page after the initial document structure was established.

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)data analysis guidelines, identifying the underlying test type is the first step in interpreting a dashboard. By observing the metrics displayed across both the Executive and IT Operations dashboards, we can definitively identify the test type asPage Load(Option B).

A Page Load test is a Web-layer test that uses a browser engine (Chromium) to fully render a page and collect advanced metrics beyond simple availability. Evidence for this test type in the dashboards includes:

Page Completion Time:Displayed on the Executive dashboard map, this metric is specific to how much of the page successfully rendered.

DOM Load Time:Found in the IT Operations dashboard, the Document Object Model (DOM) time is a browser-centric metric that measures when the page structure has finished loading.

Waterfall Charts:While not a specific answer option, these are the foundation of Page Load tests, allowing for the timing of individual web components.

SimpleHTTP Servertests (Option A) only measure availability and response codes, missing the rendering metrics seen here.Agent to server(Option C) is a network-layer test that provides path visualization but no browser-level data.FTP(Option D) is a protocol-specific test not used for web application monitoring. Therefore, the presence of DOM and Page Completion metrics confirms thePage Loadtest type.

In theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, selecting the appropriate test type is essential for isolating performance bottlenecks in complex web applications. When users report issues with specific multi-step workflows—such as logging into a portal or interacting with a shopping cart—theTransaction test type(Option C) is the most effective tool.

A Transaction test is a specialized Web-layer test that utilizes a script (typically written in JavaScript/TypeScript) to mimic real user interactions with a website. Unlike a simpleHTTP Server test(Option A) that only checks for a 200 OK response from a single URL, or aPage Load test(Option B) that measures the rendering of a single page, a Transaction test follows a predefined user journey. For this retail scenario, the test can be configured to navigate to the homepage, enter credentials, click the login button, search for a product, and add it to the cart.

The primary advantage of this approach is that it provides granular, step-specific timing. It allows the engineer to identify precisely where the latency or failure occurs—for example, if the backend database takes too long to process the login or if an API call fails during the "add to cart" action. WhileAgent-to-server(Option D) andDNS Server(Option E) tests provide valuable network and infrastructure data, they cannot validate the functional logic of a web application.Agent-to-agent(Option F) is used for measuring throughput between two managed points and is irrelevant for public-facing website testing. Therefore, for troubleshooting interactive web processes, theTransaction test typeis the definitive choice for pinpointing the source of the issue.

According to theDesigning and Implementing Enterprise Network Assurance (300-445 ENNA)curriculum, the native integration of ThousandEyes within the Meraki portfolio is specifically focused on theMeraki MXsecurity and SD-WAN appliances. This integration allows users to install ThousandEyes Enterprise Agents directly on supported Meraki MX hardware, providing critical visibility into the performance of services that distributed users rely on.4

TheMeraki MX (Option A)supports the ThousandEyes Enterprise Agent as a containerized service that can be activated with a single click within the Meraki Dashboard.5This integration is designed to provide better monitoring and testing capabilities for customers interested in improving the quality of their experience and adding the appropriate SD-WAN policies to optimize network performance. By running the agent natively on the MX, administrators can monitor the performance of external applications and services directly from the SD-WAN sites, bridging the gap between local branch health and global internet performance.6

While Cisco continues to expand its assurance integrations across the portfolio, theMR (Wireless),MS (Switching),MV (Camera), andMG (Cellular)series do not currently support the native, "on-box" execution of the ThousandEyes Enterprise Agent. For these other platforms, visibility is typically achieved through passive monitoring viaMeraki Insight (MI)or by deploying ThousandEyes agents on connected client devices (Endpoint Agents) or nearby infrastructure. Therefore, theMeraki MXis the only verified platform supporting native ThousandEyes agent deployment in this context.

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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.

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)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 totune 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.