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| Section | Objectives |
|---|---|
| Automation and Data Interpretation | - Data-driven assurance workflows
|
| Enterprise Network Assurance Architecture | - Assurance concepts and methodologies
|
| Assurance Platforms and Tools | - Cisco assurance platforms
|
| Troubleshooting and Analysis | - Performance and fault analysis
|
| Monitoring and Telemetry Technologies | - Data collection mechanisms
|
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NEW QUESTION # 23
Which Cisco technology provides real-time analytics and insights into network traffic using flow-based telemetry?
Answer: A
Explanation:
NetFlow is a Cisco technology that provides real-time analytics and insights into network traffic using flow-based telemetry. It collects and analyzes network flow data, including source and destination IP addresses, ports, protocols, and traffic volume, to monitor network behavior, detect anomalies, and troubleshoot performance issues.
NEW QUESTION # 24
What is the primary purpose of integrating ThousandEyes with Meraki?
Answer: A
Explanation:
The Designing and Implementing Enterprise Network Assurance (300-445 ENNA) framework highlights the integration between ThousandEyes and Cisco Meraki as a solution for "cross- domain assurance". The primary purpose of this integration is to monitor external applications and services from SD-WAN sites (Option B).
In a distributed Meraki environment, IT teams often struggle with visibility into the "Internet as a WAN," where performance issues may occur outside the local network perimeter. By embedding ThousandEyes Enterprise Agents natively within Meraki MX appliances, organizations can bridge the gap between internal LAN metrics and external service health. This integration allows for proactive monitoring of SaaS platforms (like Microsoft 365, Salesforce, and Webex) and other public-facing dependencies using synthetic probes. It complements the native Meraki Insight (MI), which provides passive monitoring of real user traffic, by adding active path visualization and hop- by-hop analysis across the Internet.
Key advantages of this integration include:
One-Click Activation: Enabling the ThousandEyes agent directly from the Meraki Dashboard without additional hardware.
Pre-configured Templates: Using built-in test templates for common SaaS applications to accelerate troubleshooting.
Isolation of Fault Domains: Quickly determining if a user's lag is caused by a local Wi-Fi issue (via Meraki wireless metrics) or an ISP routing problem (via ThousandEyes path data).
While ThousandEyes does provide visibility for VPN and security, Options A, C, and D are not the primary focus of the specific Meraki-ThousandEyes integration architecture, which is centered on extending application performance assurance to distributed branch locations.
NEW QUESTION # 25
Users at a remote corporate site (identified as s30 in the exhibit) are experiencing issues with a critical Enterprise Application hosted in the Data Center. The site connects to the central campus through an MPLS network.
The following exhibits show the network status before and after the issue began. Based on the information presented, what is the most likely cause of the problem and what actions would you take next as a Network Operations Engineer?
Answer: D
NEW QUESTION # 26
What is the first step in setting a metric baseline for network performance monitoring?
Answer: B
Explanation:
The first step in setting a metric baseline is defining key performance indicators (KPIs) to measure and monitor network performance in network assurance.
NEW QUESTION # 27
Refer to the exhibit.
Which integration type should be configured between ThousandEyes and Grafana?
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.
NEW QUESTION # 28
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