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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- Storage systems
      • 1. High-throughput storage for AI training data
        • 2. Distributed storage concepts
          - Compute architecture
          • 1. HPC-style scaling considerations
            • 2. GPU-based compute nodes
              Data Center Networking for AI Workloads- Cisco Data Center networking technologies
              • 1. Cisco ACI integration concepts
                • 2. Cisco Nexus platform concepts
                  - High-performance fabric design
                  • 1. Leaf-spine architecture in AI environments
                    • 2. Low-latency switching and routing considerations
                      Security and Governance in AI Data Centers- Infrastructure security
                      • 1. Network segmentation and policy enforcement
                        • 2. Secure access to AI workloads
                          AI Infrastructure Deployment and Operations- Deployment models
                          • 1. Hybrid AI infrastructure integration
                            • 2. On-premises AI clusters
                              - Operations and lifecycle management
                              • 1. Performance tuning and optimization
                                • 2. Monitoring and observability

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                                  Exam Sample 300-640 Questions - 300-640 Latest Exam Labs

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q49-Q54):

                                  NEW QUESTION # 49
                                  A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?

                                  Answer: C

                                  Explanation:
                                  For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.


                                  NEW QUESTION # 50
                                  Drag and Drop Question
                                  An engineer must configure vNIC templates for a UCS X-Series deployment. Drag and drop the policy types from the left onto the corresponding configuration settings on the right. Not all options are used.

                                  Answer:

                                  Explanation:


                                  NEW QUESTION # 51
                                  A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?

                                  Answer: B

                                  Explanation:
                                  Cisco UCS X-Series is designed for scalable, modular compute environments that support GPU- enabled workloads, high memory capacity, virtualization, and coexistence with existing infrastructure. Its modular architecture allows the enterprise to expand resources as AI workload demands grow.


                                  NEW QUESTION # 52
                                  A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

                                  Answer: A

                                  Explanation:
                                  Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.


                                  NEW QUESTION # 53
                                  To support growing AI workload requirements, a regional medical practice is adding new X9516 X-Fabric module, UCS PCI Mezz cards, and UCS X580p PCIe nodes to their existing X-Series chassis. Which two administrative actions must be taken before the X-Fabric module is added to the chassis? (Choose two.)

                                  Answer: C,E

                                  Explanation:
                                  Before adding the X9516 X-Fabric module, the affected X-Series blades must be powered down because the X-Fabric connectivity path requires hardware installation and cannot be safely introduced while the connected compute nodes are running. The UCS PCI Mezzanine cards must also be installed in the blades so they can physically connect through the X-Fabric module to the X580p PCIe nodes for GPU expansion.


                                  NEW QUESTION # 54
                                  ......

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