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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
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. 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. Data pipeline and workload characteristics
                    • 2. Compute, GPU, and acceleration concepts
                      Security and Governance in AI Data Centers- Infrastructure security
                      • 1. Network segmentation and policy enforcement
                        • 2. Secure access to AI workloads
                          AI Infrastructure Deployment and Operations- Deployment models
                          • 1. On-premises AI clusters
                            • 2. Hybrid AI infrastructure integration
                              - Operations and lifecycle management
                              • 1. Monitoring and observability
                                • 2. Performance tuning and optimization

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q65-Q70):

                                  NEW QUESTION # 65
                                  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?

                                  Answer: D

                                  Explanation:
                                  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.


                                  NEW QUESTION # 66
                                  Which IP-based storage transport supports flow control between directly connected devices?

                                  Answer: B

                                  Explanation:
                                  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.


                                  NEW QUESTION # 67
                                  Which type of Cisco Intersight profile, when deployed to fabric interconnects, includes the configuration settings for ports, VLANs, and VSANs?

                                  Answer: B

                                  Explanation:
                                  A UCS Domain profile is deployed to fabric interconnects in Cisco Intersight Managed Mode and contains the domain-level configuration, including port policies, VLANs, VSANs, network connectivity, and other fabric interconnect settings.


                                  NEW QUESTION # 68
                                  A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?

                                  Answer: B

                                  Explanation:
                                  AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.


                                  NEW QUESTION # 69
                                  A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?

                                  Answer: D

                                  Explanation:
                                  NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.


                                  NEW QUESTION # 70
                                  ......

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