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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
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
          Security and Governance in AI Data Centers- Infrastructure security
          • 1. Secure access to AI workloads
            • 2. Network segmentation and policy enforcement
              Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
              • 1. Compute, GPU, and acceleration concepts
                • 2. Data pipeline and workload characteristics
                  Compute and Storage for AI Infrastructure- Storage systems
                  • 1. Distributed storage concepts
                    • 2. High-throughput storage for AI training data
                      - Compute architecture
                      • 1. GPU-based compute nodes
                        • 2. HPC-style scaling considerations
                          AI Infrastructure Deployment and Operations- Operations and lifecycle management
                          • 1. Monitoring and observability
                            • 2. Performance tuning and optimization
                              - Deployment models
                              • 1. On-premises AI clusters
                                • 2. Hybrid AI infrastructure integration

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q13-Q18):

                                  NEW QUESTION # 13
                                  Which IP-based storage transport supports flow control between directly connected devices?

                                  Answer: C

                                  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 # 14
                                  An organization deploys a new AI training fabric that uses RoCEv2 for GPU communication. The network architect designs the QoS configuration to ensure reliable RDMA transport and must meet these requirements:
                                  - Support 256 GPU servers with RDMA connectivity.
                                  - Prevent any packet loss that causes RDMA connection failures.
                                  - Maintain consistent low-latency communication with a target of less
                                  than 10 microseconds.
                                  - Use industry-standard protocols and configurations.
                                  Which configuration ensures that RoCEv2 operates as a lossless transport?

                                  Answer: C

                                  Explanation:
                                  RoCEv2 requires lossless Ethernet behavior to prevent packet drops that can disrupt RDMA communication. Enabling Priority Flow Control on the traffic class carrying RoCEv2 packets allows Ethernet pause behavior for that priority, helping maintain reliable, low-latency RDMA transport across the AI training fabric.


                                  NEW QUESTION # 15
                                  An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?

                                  Answer: C

                                  Explanation:
                                  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.


                                  NEW QUESTION # 16
                                  A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?

                                  Answer: A

                                  Explanation:
                                  PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.


                                  NEW QUESTION # 17
                                  A medical company has existing third-party AI servers and NVMe storage, but they want to purchase Cisco network switches with a cloud managed feature for simplicity. Which solution best meets the requirements?

                                  Answer: A

                                  Explanation:
                                  Bring Your Own AI HyperFabric is the best fit when the customer already has third-party AI servers and NVMe storage but wants Cisco 6000 Series switching with simplified cloud-managed fabric operations. The Premier license aligns with the AI-focused HyperFabric use case, while Cisco describes Nexus HyperFabric as a cloud-managed AI infrastructure solution using Cisco
                                  6000 Series switches for AI workloads.


                                  NEW QUESTION # 18
                                  ......

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