実用的な300-640学習資料 &合格スムーズ300-640過去問題 |効果的な300-640無料試験Implementing Cisco Data Center AI Infrastructure

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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
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
              Compute and Storage for AI Infrastructure- Compute architecture
              • 1. HPC-style scaling considerations
                • 2. GPU-based compute nodes
                  - Storage systems
                  • 1. High-throughput storage for AI training data
                    • 2. Distributed storage concepts
                      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
                              Security and Governance in AI Data Centers- Infrastructure security
                              • 1. Network segmentation and policy enforcement
                                • 2. Secure access to AI workloads

                                  >> 300-640学習資料 <<

                                  300-640過去問題、300-640無料試験

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                                  Cisco Implementing Cisco Data Center AI Infrastructure 認定 300-640 試験問題 (Q14-Q19):

                                  質問 # 14
                                  An engineer configures Cisco Nexus Dashboard Fabric Controller for AI infrastructure automation. The company wants centralized policy management and simplified fabric provisioning across multiple pods. Which capability provides this benefit?

                                  正解：A

                                  解説：
                                  Intent-based fabric automation simplifies deployment and policy consistency across large AI infrastructures. Nexus Dashboard Fabric Controller automates provisioning, validation, and operational management. Distributed ACLs and local VLAN switching are operational features but do not centralize orchestration. Manual CLI verification is error-prone and unsuitable for rapidly scaling AI environments.


                                  質問 # 15
                                  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 current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?

                                  正解：D

                                  解説：
                                  Cisco UCS X-Series is designed for scalable, modular compute environments that support GPU- enabled workloads, high memory capacity, virtualization, and coexistence with existing infrastructure. Its modular architecture allows the enterprise to expand resources as AI workload demands grow.


                                  質問 # 16
                                  A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

                                  正解：C

                                  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 # 17
                                  What is a purpose of Cisco AI PODs?

                                  正解：B

                                  解説：
                                  Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.


                                  質問 # 18
                                  Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?

                                  正解：A

                                  解説：
                                  Rapid provisioning supports scalable AI workload execution by enabling computational platforms to be launched quickly and consistently as workload demand grows. This allows AI infrastructure to add or prepare compute resources efficiently for training, inference, or data-processing tasks.


                                  質問 # 19
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