ユニーク300-640｜便利な300-640最新対策問題試験｜試験の準備方法Implementing Cisco Data Center AI Infrastructure試験復習

300-640試験問題は正式に認定されています。 私たちCiscoの目標は、効率的な学習スタイルで、Implementing Cisco Data Center AI Infrastructure関連する300-640試験に合格できるようにすることです。 300-640トレーニング資料の品質と手頃な価格により、当社の競争力は常に世界のリーダーです。 300-640学習教材は、他のトレーニング教材よりも高い合格率を持っているため、完全な結果を得ることができると確信しています。 300-640試験問題を使用すると、JPTestKing成功が保証されます。

Cisco 300-640 Exam Syllabus Topics:

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

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                                  Cisco Implementing Cisco Data Center AI Infrastructure 認定 300-640 試験問題 (Q63-Q68):

                                  質問 # 63
                                  A solutions architect must implement a monitoring strategy for a new AI/ML data center infrastructure built on Cisco UCS X-Series servers with NVIDIA H100 GPUs, managed by Cisco Intersight. The main objectives are to gain deep visibility into GPU performance, identify resource bottlenecks for AI training workloads, and ensure the overall health and efficiency of the compute environment. Which Cisco Intersight capability is most critical for effectively monitoring this AI- centric infrastructure?

                                  正解：D

                                  解説：
                                  Cisco Intersight provides health and performance monitoring for UCS servers, including real-time telemetry and GPU-specific metrics for NVIDIA GPUs. This visibility is essential for identifying bottlenecks, tracking AI training workload behavior, and maintaining the health and efficiency of GPU-accelerated UCS X-Series infrastructure.


                                  質問 # 64
                                  An engineer notices excessive broadcast traffic inside a legacy AI server VLAN. Which technology should be implemented to reduce flooding and improve scalability?

                                  正解：C

                                  解説：
                                  VXLAN EVPN reduces unknown unicast and broadcast flooding by using control-plane learning with BGP EVPN. This improves scalability in modern AI data centers. RIP is outdated for large- scale fabrics, while PAT and GRE tunneling do not address broadcast domain limitations.


                                  質問 # 65
                                  What is the process involved in workload scheduling for AI environments?

                                  正解：D

                                  解説：
                                  Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.


                                  質問 # 66
                                  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：A

                                  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 # 67
                                  A customer deploys a UCS 885a server in a data center with two power grids. Three of the
                                  3000W power supplies in the server connect to each grid. Due to an external event, one of the grids will be shut down for maintenance. How does the server respond to this event?

                                  正解：D

                                  解説：
                                  With three 3000W power supplies lost from one power grid, the UCS C885A continues operating on the remaining grid but no longer has the full power budget available for maximum GPU operation. The server reduces performance by enforcing power limits, including capping GPU power, so workloads can continue running within the available power capacity.


                                  質問 # 68
                                  ......

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