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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
      Data Center Networking for AI Workloads- High-performance fabric design
      • 1. Leaf-spine architecture in AI environments
        • 2. Low-latency switching and routing considerations
          - Cisco Data Center networking technologies
          • 1. Cisco ACI integration concepts
            • 2. Cisco Nexus platform concepts
              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- 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. 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 (Q72-Q77):

                                  NEW QUESTION # 72
                                  A Cisco Nexus switch is configured with QoS policies for AI traffic. Which scheduling mechanism guarantees bandwidth for critical traffic classes during congestion?

                                  Answer: C

                                  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 # 73
                                  What is a primary behavior that occurs during the model training stage of the AI lifecycle?

                                  Answer: D

                                  Explanation:
                                  During model training, the model learns from training data by iteratively adjusting its internal weights and biases to reduce prediction error and improve performance against the defined objective function.


                                  NEW QUESTION # 74
                                  Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?

                                  Answer: A

                                  Explanation:
                                  A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.


                                  NEW QUESTION # 75
                                  A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?

                                  Answer: C

                                  Explanation:
                                  A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.


                                  NEW QUESTION # 76
                                  A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?

                                  Answer: C

                                  Explanation:
                                  Internet Group Management Protocol (IGMP) allows hosts to join and leave multicast groups dynamically. This improves multicast efficiency for distributed AI workloads. ARP resolves MAC addresses, while LLDP and CDP are neighbor discovery protocols that do not manage multicast memberships.


                                  NEW QUESTION # 77
                                  ......

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