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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Deployment | 25% | - Hardware Installation and Configuration - Storage Configuration for AI - GPU Server Configuration - Cabling and Connectivity - Network Switch Configuration |
| AI Infrastructure Security and Compliance | 10% | - Compliance Considerations - Access Control and Authentication - Security Best Practices |
| AI Infrastructure Operations | 20% | - Performance Optimization - Troubleshooting Common Issues - Capacity Planning - Monitoring and Management Tools |
| Cisco AI Infrastructure Architecture | 30% | - Storage Architecture Considerations - Network Architecture for AI Workloads - Cisco UCS AI Infrastructure Solutions - Hyperconverged Infrastructure for AI |
| AI Infrastructure Fundamentals | 15% | - AI Infrastructure Requirements - AI Ecosystem Components - GPU Architecture and Types - AI Workload Characteristics |
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질문 # 12
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?
정답：D
설명：
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.
질문 # 13
An end customer plans to deploy an AI fabric in a new data center. Their engineers are more familiar with CLI-based networks and require the capability to automate standardized configuration templates and monitor flows for the fabric. Which data center automation tool meets the requirements?
정답：C
설명：
Nexus Dashboard is the Cisco data center automation and operations platform used with Nexus fabrics. It supports standardized fabric configuration through automation templates and provides visibility features such as flow monitoring, making it the appropriate tool for engineers who need CLI-oriented data center fabric operations with automation and telemetry.
질문 # 14
A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
- policy managed through cloud-based interface
- low latency between specific pairs of GPUs
- at least 2 GPUs per server
- all GPUs should be same model
- Intel CPU
- well suited for Inferencing
- customer is power conscious
Which server meets the requirements?
정답：D
설명：
The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
Reference:
https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf
질문 # 15
A Cisco AI environment requires synchronized packet processing timestamps across network devices for analytics accuracy. Which protocol should be deployed?
정답：B
설명：
Precision Time Protocol (PTP) provides highly accurate clock synchronization required for latency-sensitive AI and analytics environments. NTP offers lower precision suitable for general IT systems. FTP and DNS are unrelated to time synchronization.
질문 # 16
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?
정답：C
설명：
Microbursts occur within milliseconds and are often missed by traditional monitoring methods.
High-frequency streaming telemetry provides granular visibility into queue depth, drops, and latency conditions in real time. Hourly exports and infrequent manual inspection cannot reliably detect transient AI fabric congestion events.
질문 # 17
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