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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
AI Infrastructure Deployment and Operations- Deployment models
  • 1. On-premises AI clusters
    • 2. Hybrid AI infrastructure integration
      - Operations and lifecycle management
      • 1. Monitoring and observability
        • 2. Performance tuning and optimization
          Compute and Storage for AI Infrastructure- Compute architecture
          • 1. GPU-based compute nodes
            • 2. HPC-style scaling considerations
              - Storage systems
              • 1. High-throughput storage for AI training data
                • 2. Distributed storage 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- 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
                              Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
                              • 1. Data pipeline and workload characteristics
                                • 2. Compute, GPU, and acceleration concepts

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q14-Q19):

                                  NEW QUESTION # 14
                                  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: B

                                  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 # 15
                                  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: B

                                  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 # 16
                                  What is a purpose of Cisco AI PODs?

                                  Answer: A

                                  Explanation:
                                  Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.


                                  NEW QUESTION # 17
                                  When considering a rail-only network design, when would adding spine switches make sense?

                                  Answer: D

                                  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 # 18
                                  A Cisco AI cluster requires segmentation between multiple research teams sharing the same physical infrastructure. Which solution provides scalable tenant isolation?

                                  Answer: A

                                  Explanation:
                                  VXLAN overlays provide scalable multi-tenant segmentation using large VNI spaces beyond traditional VLAN limitations. AI infrastructures often support many isolated environments simultaneously. VLAN-only designs have scalability constraints, while WAN topologies and NAT do not provide modern data center tenant segmentation.


                                  NEW QUESTION # 19
                                  ......

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