300-640最新試験情報 & 300-640トレーニング資料

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Cisco 300-640 Exam Syllabus Topics:

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

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                                  Cisco Implementing Cisco Data Center AI Infrastructure 認定 300-640 試験問題 (Q26-Q31):

                                  質問 # 26
                                  A company is building a network fabric for an AI training cluster that will train large language models. The cluster includes 256 GPUs distributed across 32 servers. Security requirements mandate isolation between different training projects and allowing shared access to a central storage system. Which approach provides the required security isolation and maintains optimal performance for GPU-to-GPU communication?

                                  正解:D

                                  解説:
                                  VXLAN provides scalable network segmentation by assigning separate VNIs to different training projects, preserving isolation without building separate physical fabrics. Group Policy Option adds policy-based control within the VXLAN overlay, allowing controlled shared access to central storage while maintaining high-performance GPU-to-GPU communication across the AI training fabric.


                                  質問 # 27
                                  A data center operator configures telemetry streaming on Cisco Nexus switches. What is the primary operational advantage over traditional SNMP polling?

                                  正解:B

                                  解説:
                                  Streaming telemetry continuously exports operational data in near real time, improving visibility and reducing polling overhead compared to SNMP. AI infrastructures require rapid monitoring of congestion and latency conditions. Telemetry does not remove routing tables or require hardware replacement.


                                  質問 # 28
                                  A network engineer is deploying an AI training fabric using Cisco Nexus switches with RoCEv2 enabled. During testing, packet drops occur when traffic bursts exceed buffer capacity. Which feature should be configured to minimize congestion and improve lossless Ethernet performance for GPU traffic?

                                  正解:C

                                  解説:
                                  Priority Flow Control (PFC) allows selective pausing of traffic classes during congestion, which is critical for RoCEv2 environments carrying GPU-to-GPU communication. AI workloads are highly sensitive to packet loss and retransmissions. BPDU Guard and DHCP Snooping are security features, while Storm Control only limits broadcast or multicast traffic and does not create lossless behavior for RDMA traffic.


                                  質問 # 29
                                  What describes inference traffic patterns in AI deployments?

                                  正解:D

                                  解説:
                                  Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.


                                  質問 # 30
                                  A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

                                  正解:B

                                  解説:
                                  Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.


                                  質問 # 31
                                  ......

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