Languages English
Duration 90 minutes
Price $US300, or use Cisco Learning Credits
Certifications Cisco Certified Network Professional (CCNP) Enterprise
Cisco Certified Specialist – Enterprise Network Assurance
Overview
Passing this exam earns you the Cisco Certified Specialist – Enterprise Network Assurance certification, and can also be used to meet the concentration exam requirements for the Cisco Certified Networking Professional (CCNP) Enterprise certification. Passing this exam also can be used towards your recertification goals.
Designing and Implementing Enterprise Network Assurance (300-445 ENNA) v1.0 is a 90-minute exam that assesses your knowledge of network assurance design and implementation, covering platforms and architecture, data collection and analysis, and more.
Exam Description:
Designing and Implementing Enterprise Network Assurance v1.0 (ENNA 300-445) is a 90-minute exam associated with the CCNP Enterprise Certification. This exam certifies a candidate’s knowledge of network assurance design and implementation, including platforms and architecture, data collection and implementation, data analysis, and insights and alerts. The following topics are general guidelines for the content likely to be included on the exam. However, other related topics may also appear on any specific delivery of the exam. To better reflect the contents of the exam and for clarity purposes, the guidelines below may change at any time without notice.
20% 1.0 Platforms and Architecture
1.1 Determine agent types, such as synthetic user agent, scripting agent, and local collection agent to meet network assurance and security requirements
1.2 Determine agent location to meet network assurance and security requirements
1.3 Describe active and passive monitoring (RFC 7276 and RFC 7799)
1.4 Describe ThousandEyes WAN Insights
1.5 Describe the integration between Cisco technologies, such as ThousandEyes, Catalyst SD-WAN Manager, Catalyst Center, Webex Control Hub, Meraki, and Endpoint Agent deployment through Secure Client
1.6 Describe setting a metric baseline
1.7 Select the integration type, such as API, alerting thresholds, open telemetry, and ITSM for the requested data
1.8 Select a Cisco network assurance platform based on business requirements, such as application communication, user experience, web, and events
25% 2.0 Data Collection Implementation
2.1 Configure enterprise agent on application servers and network infrastructure devices, including dedicated devices
2.2 Describe endpoint agent deployment at scale across the enterprise on end-user devices (Windows, Mac, and Room OS)
2.3 Configure tests using tools, such as ThousandEyes and Meraki Insights
2.3.a Network such as TCP/UDP, network characteristics, loss, jitter, and latency
2.3.b DNS
2.3.c Voice
2.3.d Web
2.4 Configure endpoint agent tests in ThousandEyes
2.5 Describe the purpose, implementation, and limitations of synthetic web tests
2.6 Implement common web authentication methods, such as basic, digest, bearer tokens, OAuth, SAML, and SSO when testing web applications
30% 3.0 Data Analysis
3.1 Diagnose network issues, such as packet loss, congestion, routing, and jitter using collected data
3.2 Diagnose end-device network issues, such as issues with a default gateway, local network, DNS server, proxy, VPN gateway, wireless, and real-time streaming using collected data
3.3 Diagnose web application performance issues using collected data such as browser waterfalls
3.4 Identify security issues, such as DDoS attacks, DNS hijacking, BGP hijacking, and route leaking affecting network performance
25% 4.0 Insights and Alerts
4.1 Configure alert rules based on network conditions, such as TCP protocol behavior, congestion, error counters, performance, throughput, state of BGP routing table, internet insights, MPLS, VPN, NetFlow, SNMP, and syslog
4.2 Configure alert rules that affect the end-user experience, such as CPU utilization, connectivity types (wired to wireless, Wi-Fi), browser behavior, and VPN
4.3 Select deliverables or metrics such as dashboard and alerts for IT operations, production support, app/dev teams, and executives
4.4 Validate alert configuration and functionality
4.5 Recommend optimization for network capacity planning, such as topology and configuration changes, and QoS based on data interpretation
Prepare for your exam
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Sample Question and Answers
QUESTION 1
Exhibit:
An engineer works to optimize a website by reducing the page-load time to below 500 ms
The engineer set up a Cisco ThousandEyes page-load test to baseline the current website performance.
Which action should be recommended to reduce page-load time?
A. Optimize the AJAX query calling functions.
B. Move IMG elements to the bottom of the document body.
C. Implement lazy loading for objects on the page.
D. Use a CDN to load fonts faster.
Answer: C
Explanation:
In the context of Designing and Implementing Enterprise Network Assurance (300-445 ENNA),
analyzing page-load metrics within Cisco ThousandEyes requires identifying the primary bottlenecks
that contribute to the Total Page Load Time. The provided screenshot displays a “Page Breakdown” of
7 resources totaling 953 kB. A critical observation of the pie chart reveals that Images (the tealcolored
segment) constitute the vast majority of the page’s payload and resource count.
When the goal is to reduce the page-load time from 1023 ms to below 500 ms, the engineer must
target the heaviest components. Lazy loading is a design pattern that defers the initialization of noncritical
resources at page load time. Instead of loading all images simultaneously when the user first
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, implementing lazy 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.
QUESTION 2
Refer to Exhibit:
A network engineer is deploying a Cisco ThousandEyes agent to monitor the network for a SaaS
application without affecting the performance of the employee endpoints.
Which ThousandEyes agent must be deployed to obtain the network metrics from branch A?
A. Endpoint Agent
B. Application Agent
C. Enterprise Agent
D. Cloud Agent
Answer: C
Explanation:
In the framework of Designing and Implementing Enterprise Network Assurance (300-445 ENNA),
selecting the appropriate ThousandEyes agent type is critical to balancing visibility requirements
with infrastructure constraints. For Branch A, the primary objective is to gain network-layer metrics
(such as latency, packet loss, and jitter) and path visualization for a SaaS application while strictly
avoiding any performance impact on employee endpoints.
The Enterprise Agent (Option C) is the correct choice because it is designed for “inside-out”
monitoring from within the corporate network environment. These agents are lightweight software
probes that can be deployed on existing network infrastructure, such as Cisco Catalyst 930000
switches or Catalyst 8000 Edge Platforms, using Docker containers or virtual machines. By hosting the
agent on the branch router or a dedicated local server, the engineer can execute synthetic tests to
the SaaS provider’s destination. This approach provides the necessary network vantage point from
Branch A without requiring any software installation or resource consumption on the individual
employee workstations (endpoints).
Other agent types do not satisfy the specific constraints of this scenario:
Endpoint Agents are installed directly on user devices (Windows/macOS) to provide “last-mile”
visibility. However, they use the endpoint’s CPU and memory, which contradicts the requirement to
not affect endpoint performance.
Cloud Agents are maintained by Cisco in global ISP data centers. While they provide “outside-in”
visibility, they cannot capture internal branch network characteristics or the specific path from
Branch A’s internal local area network.
Application Agent is a non-standard term and does not exist as a standalone agent type within the
ThousandEyes architecture.
Therefore, deploying an Enterprise Agent within the branch infrastructure ensures that the network
engineer obtains high-fidelity network metrics while keeping employee devices entirely unburdened.
Introduction to ThousandEyes
This video provides an essential overview of how ThousandEyes agents function within a CCNP-level
enterprise network assurance strategy.
QUESTION 3
Refer to the exhibit.
Which integration type should be configured between ThousandEyes and Grafana?
A. opentelemetry
B. custom-webhook
C. push-api
D. poll-api
Answer: A
Explanation:
In the Designing 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 like Grafana, the
standardized and recommended method for machine-to-machine data exchange is through
OpenTelemetry (OTel).
According to the ENNA architecture guidelines, the ThousandEyes Streaming API allows 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-api and poll-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 selecting opentelemetry, 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.
QUESTION 4
DRAG DROP
Drag and drop the Cisco Network Assurance platforms from the left onto the corresponding business cases on the right.
Answer:
Explanation:
AppDynamics
Catalyst Center
ThousandEyes
Meraki
In the Designing and Implementing Enterprise Network Assurance (300-445 ENNA) architecture,
each platform is positioned to address a distinct domain of visibility and management within the
modern IT visibility framework.
AppDynamics (matched to Case 1) is specifically engineered for Full-Stack Observability and
Application Performance Monitoring (APM). Its unique focus is linking technical application
performance”such as code-level execution and database queries”directly to business outcomes
and real-time business metrics. By doing so, it allows organizations to pinpoint how application
latency impacts revenue or user satisfaction, making it the primary choice for business impact analysis.
Catalyst Center, formerly DNA Center (matched to Case 2), serves as the foundational controller for
campus and enterprise network infrastructure. It leverages AI-driven insights to automate network
operations, enforce security policies, and provide at-a-glance health monitoring for local devices and
clients. It is the definitive management system for connecting and securing “inside” the corporate
network perimeter.
ThousandEyes (matched to Case 3) provides “Internet Intelligence” and is the key solution for
monitoring the service delivery chain across environments the enterprise does not own. By utilizing a
global network of Cloud, Enterprise, and Endpoint agents, it provides end-to-end visibility from any
user location to any application (SaaS or Cloud) over any network (Internet, ISP, or WAN).
Meraki (matched to Case 4) is a cloud-first platform designed for managing highly distributed
network environments and IoT deployments. It provides end-to-end visibility through a simplified
dashboard that integrates wireless health, switching, and security across thousands of sites, making
it ideal for lean IT teams managing distributed retail or branch locations.
Each platform utilizes different data collection methods”active synthetic testing for ThousandEyes
and passive monitoring/AI telemetry for Catalyst Center”to ensure comprehensive network
assurance across the entire enterprise ecosystem.
QUESTION 5
Refer to the exhibit.
An engineer must use Cisco ThousandEyes testing to monitor their Cisco Catalyst SD-WAN fabric.
Which SD-WAN component is being monitored by ThousandEyes?
A. underlay
B. IPsec tunnels
C. overlay
D. GRE tunnels
Answer: A
Explanation:
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-byhop
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
