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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Infrastructure Deployment and Operations- Operations and lifecycle management
  • 1. Monitoring and observability
    • 2. Performance tuning and optimization
      - Deployment models
      • 1. Hybrid AI infrastructure integration
        • 2. On-premises AI clusters
          Topic 2: Data Center Networking for AI Workloads- Cisco Data Center networking technologies
          • 1. Cisco Nexus platform concepts
            • 2. Cisco ACI integration concepts
              - High-performance fabric design
              • 1. Low-latency switching and routing considerations
                • 2. Leaf-spine architecture in AI environments
                  Topic 3: Compute and Storage for AI Infrastructure- Storage systems
                  • 1. Distributed storage concepts
                    • 2. High-throughput storage for AI training data
                      - Compute architecture
                      • 1. HPC-style scaling considerations
                        • 2. GPU-based compute nodes
                          Topic 4: Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
                          • 1. Data pipeline and workload characteristics
                            • 2. Compute, GPU, and acceleration concepts
                              Topic 5: Security and Governance in AI Data Centers- Infrastructure security
                              • 1. Secure access to AI workloads
                                • 2. Network segmentation and policy enforcement

                                  >> 300-640日本語版 <<

                                  ユニーク-ハイパスレートの300-640日本語版試験-試験の準備方法300-640日本語受験攻略

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                                  Cisco Implementing Cisco Data Center AI Infrastructure 認定 300-640 試験問題 (Q39-Q44):

                                  質問 # 39
                                  A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
                                  Which set of products meets the requirements?

                                  正解：B

                                  解説：
                                  Cisco UCS C885A M8 with NVIDIA H200 GPUs is designed for high-performance AI workloads that require dense GPU acceleration, large memory capacity, and direct GPU-to-GPU communication through NVIDIA NVLink. This combination supports demanding analytics, training, and inference workloads where high-bandwidth GPU interconnect is required.


                                  質問 # 40
                                  An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?

                                  正解：D

                                  解説：
                                  RoCEv2 depends on low-latency and near-lossless Ethernet transport to maintain efficient RDMA communication. Packet drops trigger retransmissions and significantly degrade AI training performance. Best-effort forwarding cannot guarantee delivery quality, while fragmentation and randomized path selection do not address the primary RDMA requirement.


                                  質問 # 41
                                  A network architect wants to provide high availability for AI compute nodes connected to dual Cisco Nexus switches. Which technology enables active-active uplink forwarding without STP blocking?

                                  正解：C

                                  解説：
                                  Virtual Port Channel (vPC) allows devices to use active-active uplinks across two Nexus switches while avoiding spanning-tree blocking. This increases bandwidth utilization and resiliency for AI clusters. GLBP and VRRP provide gateway redundancy, while PVST+ still relies on blocking redundant paths.


                                  質問 # 42
                                  An engineer must optimize the performance of an AI inference workload running on a Cisco UCS C-Series rack server. The workload experiences intermittent latency spikes, and Intersight system logs show frequent thermal event entries. Which troubleshooting action must be taken first to address the performance issues on the Cisco UCS server?

                                  正解：A

                                  解説：
                                  Frequent thermal events indicate that the server may be throttling CPU or GPU performance to protect hardware from overheating. The first action is to verify the physical cooling conditions, including ambient temperature, airflow, and fan health, because resolving thermal constraints directly addresses the likely cause of intermittent latency spikes.


                                  質問 # 43
                                  Drag and Drop Question
                                  A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.

                                  正解：

                                  解説：


                                  質問 # 44
                                  ......

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