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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Security and Governance in AI Data Centers- Infrastructure security
  • 1. Network segmentation and policy enforcement
    • 2. Secure access to AI workloads
      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
              Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
              • 1. Data pipeline and workload characteristics
                • 2. Compute, GPU, and acceleration concepts
                  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. Leaf-spine architecture in AI environments
                        • 2. Low-latency switching and routing considerations
                          AI Infrastructure Deployment and Operations- Operations and lifecycle management
                          • 1. Monitoring and observability
                            • 2. Performance tuning and optimization
                              - Deployment models
                              • 1. Hybrid AI infrastructure integration
                                • 2. On-premises AI clusters

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q26-Q31):

                                  NEW QUESTION # 26
                                  A Cisco Nexus switch is configured with QoS policies for AI traffic. Which scheduling mechanism guarantees bandwidth for critical traffic classes during congestion?

                                  Answer: A

                                  Explanation:
                                  Weighted scheduling allocates bandwidth according to configured priorities, ensuring critical AI traffic receives adequate forwarding resources during congestion. FIFO provides no prioritization, while MAC filtering and port mirroring are unrelated to congestion management or QoS scheduling.


                                  NEW QUESTION # 27
                                  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: B

                                  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 # 28
                                  A network architect wants to provide high availability for AI compute nodes connected to dual Cisco Nexus switches. Which technology enables active-active uplink forwarding without STP blocking?

                                  Answer: B

                                  Explanation:
                                  Virtual Port Channel (vPC) allows devices to use active-active uplinks across two Nexus switches while avoiding spanning-tree blocking. This increases bandwidth utilization and resiliency for AI clusters. GLBP and VRRP provide gateway redundancy, while PVST+ still relies on blocking redundant paths.


                                  NEW QUESTION # 29
                                  What is a function of NVLink technology within a high-performance AI server or cluster?

                                  Answer: D

                                  Explanation:
                                  NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.


                                  NEW QUESTION # 30
                                  What describes inference traffic patterns in AI deployments?

                                  Answer: C

                                  Explanation:
                                  Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.


                                  NEW QUESTION # 31
                                  ......

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