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Cisco 300-640 Exam Syllabus Topics:

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

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                                  Cisco 300-640 Exam | New 300-640 Test Pattern - Help you Prepare 300-640: Implementing Cisco Data Center AI Infrastructure Exam Easily

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q53-Q58):

                                  NEW QUESTION # 53
                                  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: B

                                  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 # 54
                                  A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?

                                  Answer: D

                                  Explanation:
                                  RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.


                                  NEW QUESTION # 55
                                  A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?

                                  Answer: B

                                  Explanation:
                                  Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.


                                  NEW QUESTION # 56
                                  An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?

                                  Answer: A


                                  NEW QUESTION # 57
                                  A network team is configuring quality of service (QoS) for RoCEv2 traffic on a VXLAN fabric. The team must ensure that congestion notification packets (CNP) generated by storage targets are never dropped in the network, as these packets are critical for congestion control feedback. How must CNP packets be handled in the QoS configuration to ensure reliable delivery?

                                  Answer: C

                                  Explanation:
                                  CNP packets provide congestion feedback for RoCEv2 traffic and must be delivered reliably so senders can reduce their transmission rate when congestion is detected. Classifying CNP packets into a high-priority queue based on their DSCP marking ensures that these control packets receive preferential treatment and are not dropped during congestion.


                                  NEW QUESTION # 58
                                  ......

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