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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations | 20% | - Monitoring and Management Tools - Troubleshooting Common Issues - Performance Optimization - Capacity Planning |
| Topic 2: AI Infrastructure Deployment | 25% | - Hardware Installation and Configuration - Network Switch Configuration - GPU Server Configuration - Cabling and Connectivity - Storage Configuration for AI |
| Topic 3: AI Infrastructure Fundamentals | 15% | - GPU Architecture and Types - AI Ecosystem Components - AI Workload Characteristics - AI Infrastructure Requirements |
| Topic 4: AI Infrastructure Security and Compliance | 10% | - Compliance Considerations - Access Control and Authentication - Security Best Practices |
| Topic 5: Cisco AI Infrastructure Architecture | 30% | - Cisco UCS AI Infrastructure Solutions - Hyperconverged Infrastructure for AI - Network Architecture for AI Workloads - Storage Architecture Considerations |
GoShikenを通じて最新のCiscoの300-640試験の問題と解答早めにを持てて、弊社の問題集があればきっと君の強い力になります。
質問 # 55
Which IP-based storage transport supports flow control between directly connected devices?
正解：B
解説：
NVMe/RoCE uses RDMA over Converged Ethernet and relies on lossless Ethernet behavior, including priority-based flow control between directly connected devices. This makes it suitable for high-performance AI storage traffic that needs low latency and minimal packet loss.
質問 # 56
A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?
正解：B
解説：
For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.
質問 # 57
A network architect designs connectivity for a new Cisco AI POD operating in Intersight Managed Mode. To ensure high availability and aggregate bandwidth for external network traffic, a port channel must be established using multiple 100 GbE uplink ports on the fabric interconnects to connect to the upstream core switch infrastructure. Which specific policy, applied through the domain profile, must be modified to define and configure this port channel on the fabric interconnects for the desired uplink connectivity?
正解：D
解説：
In Cisco Intersight Managed Mode, fabric interconnect port roles and uplink port channels are defined in the port policy associated with the UCS domain profile. Modifying the port policy allows the engineer to configure multiple 100 GbE uplink ports as a port channel for aggregated bandwidth and high availability toward the upstream network.
質問 # 58
An Intersight administrator plans to deploy new server solutions to several small branch offices across the country. Each location needs at least one chassis. The servers must have redundant CPUs, memory, and 200 Gbps of unified fabric connectivity per compute node. Which hybrid AI compute solution meets the requirements?
正解：A
解説：
Cisco UCS X-Direct is designed for smaller distributed or branch deployments that still need chassis-based UCS X-Series compute capabilities. It supports redundant compute resources and high-speed unified fabric connectivity, including 200 Gbps per compute node, making it the best fit for hybrid AI workloads across multiple branch office locations.
質問 # 59
A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?
正解：B
解説：
ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.
質問 # 60
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