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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
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
          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 Nexus platform concepts
                    • 2. Cisco ACI integration concepts
                      AI Infrastructure Deployment and Operations- Deployment models
                      • 1. On-premises AI clusters
                        • 2. Hybrid AI infrastructure integration
                          - Operations and lifecycle management
                          • 1. Performance tuning and optimization
                            • 2. Monitoring and observability
                              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 (Q58-Q63):

                                  NEW QUESTION # 58
                                  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 # 59
                                  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: C

                                  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 # 60
                                  A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?

                                  Answer: D

                                  Explanation:
                                  AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.


                                  NEW QUESTION # 61
                                  Which components are part of the HyperFabric AI solution?

                                  Answer: D

                                  Explanation:
                                  HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.


                                  NEW QUESTION # 62
                                  Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?

                                  Answer: D

                                  Explanation:
                                  Rapid provisioning supports scalable AI workload execution by enabling computational platforms to be launched quickly and consistently as workload demand grows. This allows AI infrastructure to add or prepare compute resources efficiently for training, inference, or data-processing tasks.


                                  NEW QUESTION # 63
                                  ......

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