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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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
          Topic 2: Security and Governance in AI Data Centers- Infrastructure security
          • 1. Network segmentation and policy enforcement
            • 2. Secure access to AI workloads
              Topic 3: Compute and Storage for AI Infrastructure- Storage systems
              • 1. High-throughput storage for AI training data
                • 2. Distributed storage concepts
                  - Compute architecture
                  • 1. GPU-based compute nodes
                    • 2. HPC-style scaling considerations
                      Topic 4: Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
                      • 1. Data pipeline and workload characteristics
                        • 2. Compute, GPU, and acceleration concepts
                          Topic 5: Data Center Networking for AI Workloads- High-performance fabric design
                          • 1. Leaf-spine architecture in AI environments
                            • 2. Low-latency switching and routing considerations
                              - Cisco Data Center networking technologies
                              • 1. Cisco Nexus platform concepts
                                • 2. Cisco ACI integration concepts

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

                                  NEW QUESTION # 14
                                  A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
                                  - policy managed through cloud-based interface
                                  - low latency between specific pairs of GPUs
                                  - at least 2 GPUs per server
                                  - all GPUs should be same model
                                  - Intel CPU
                                  - well suited for Inferencing
                                  - customer is power conscious
                                  Which server meets the requirements?

                                  Answer: D

                                  Explanation:
                                  The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
                                  Reference:
                                  https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
                                  https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf


                                  NEW QUESTION # 15
                                  A data center team enables jumbo frames in a Cisco AI cluster. What is the primary benefit of increasing the MTU for east-west GPU traffic?

                                  Answer: C

                                  Explanation:
                                  Jumbo frames reduce the number of packets needed to transmit large AI datasets, decreasing CPU interrupts and protocol overhead. This improves throughput and efficiency for GPU communication. Larger MTUs do not directly affect convergence or eliminate buffering, and they typically reduce rather than increase processing overhead.


                                  NEW QUESTION # 16
                                  A nationwide grocery chain has deployed Unified Edge to all its locations. Each store has 10 or more surveillance cameras that write approximately 1 Gb per hour. The surveillance retention policy requires maintaining 2 weeks of recording per camera. Which type of cost-effective centralized storage solution must be evaluated with Unified Edge devices to adhere to this policy?

                                  Answer: B

                                  Explanation:
                                  Unified Edge deployments that need centralized, cost-effective retention for large surveillance data volumes should evaluate IP-based shared storage. It provides scalable network-accessible capacity for multiple edge devices without requiring specialized Fibre Channel or InfiniBand infrastructure, making it appropriate for distributed video retention workloads.


                                  NEW QUESTION # 17
                                  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 # 18
                                  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 # 19
                                  ......

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