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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
      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. Low-latency switching and routing considerations
            • 2. Leaf-spine architecture in AI environments
              - Cisco Data Center networking technologies
              • 1. Cisco ACI integration concepts
                • 2. Cisco Nexus platform concepts
                  AI Infrastructure Deployment and Operations- Deployment models
                  • 1. Hybrid AI infrastructure integration
                    • 2. On-premises AI clusters
                      - Operations and lifecycle management
                      • 1. Monitoring and observability
                        • 2. Performance tuning and optimization
                          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

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q32-Q37):

                                  NEW QUESTION # 32
                                  Which IP-based storage transport supports flow control between directly connected devices?

                                  Answer: D

                                  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 # 33
                                  What is the process involved in workload scheduling for AI environments?

                                  Answer: B

                                  Explanation:
                                  Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.


                                  NEW QUESTION # 34
                                  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: D

                                  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 # 35
                                  An engineer must monitor congestion, flow latency, and traffic drops using Cisco Nexus Dashboard and chooses to use the Traffic Analytics feature. Which Nexus Dashboard feature is automatically disabled when Traffic Analytics is enabled?

                                  Answer: A

                                  Explanation:
                                  Traffic Analytics and Flow Telemetry use overlapping telemetry resources in Cisco Nexus Dashboard. When Traffic Analytics is enabled to monitor congestion, latency, and drops, Flow Telemetry is automatically disabled to avoid resource and data collection conflicts.


                                  NEW QUESTION # 36
                                  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 # 37
                                  ......

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