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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
  • 1. Compute, GPU, and acceleration concepts
    • 2. Data pipeline and workload characteristics
      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
              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
                      Compute and Storage for AI Infrastructure- Storage systems
                      • 1. Distributed storage concepts
                        • 2. High-throughput storage for AI training data
                          - Compute architecture
                          • 1. HPC-style scaling considerations
                            • 2. GPU-based compute nodes
                              Security and Governance in AI Data Centers- Infrastructure security
                              • 1. Network segmentation and policy enforcement
                                • 2. Secure access to AI workloads

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q50-Q55):

                                  NEW QUESTION # 50
                                  Server administrators receive reports of power utilization being near capacity across multiple UCS X-Series servers in the data center after a hardware upgrade. The chassis operates with six power supply units in redundant power mode. How can the administrator use additional power without impacting redundancy?

                                  Answer: B

                                  Explanation:
                                  Extended power capacity allows a UCS X-Series chassis with six power supplies in redundant mode to make additional power available while still preserving redundancy. This is used when power demand increases after hardware upgrades and the administrator needs more usable power without changing the redundant power configuration.


                                  NEW QUESTION # 51
                                  When considering a rail-only network design, when would adding spine switches make sense?

                                  Answer: A

                                  Explanation:
                                  In a rail-only design, each scalable unit provides optimized east-west GPU communication within that unit. Spine switches are added when the design must scale beyond a single scalable unit and support traffic between multiple scalable units while preserving predictable performance for larger AI training environments.


                                  NEW QUESTION # 52
                                  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: D

                                  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 # 53
                                  A financial company plans to deploy an AI training and inference infrastructure to support its customers. The team must use a solution that provides 96 GPUs, aligns with NVIDIA-compliant reference architectures, and enables cloud-based management of the entire on-premises network environment. Which Cisco solution meets the requirements?

                                  Answer: B

                                  Explanation:
                                  Cisco HyperFabric AI Small AI Cluster matches the 96-GPU requirement because the validated HyperFabric reference architecture uses 12 Cisco UCS C885A M8 servers with NVIDIA HGX for a total of 96 GPUs. Cisco Nexus HyperFabric is also cloud-managed and compliant with NVIDIA Enterprise Reference Architecture, enabling centralized management of the on-premises AI network, GPU servers, and storage.


                                  NEW QUESTION # 54
                                  What is the process involved in workload scheduling for AI environments?

                                  Answer: A

                                  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 # 55
                                  ......

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