Free PDF Cisco - 300-640 - Useful Implementing Cisco Data Center AI Infrastructure Valid Test Notes

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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Infrastructure Deployment and Operations- Deployment models
  • 1. Hybrid AI infrastructure integration
    • 2. On-premises AI clusters
      - Operations and lifecycle management
      • 1. Monitoring and observability
        • 2. Performance tuning and optimization
          Topic 2: 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
                  Topic 3: Security and Governance in AI Data Centers- Infrastructure security
                  • 1. Network segmentation and policy enforcement
                    • 2. Secure access to AI workloads
                      Topic 4: 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. Low-latency switching and routing considerations
                            • 2. Leaf-spine architecture in AI environments
                              Topic 5: 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 (Q77-Q82):

                                  NEW QUESTION # 77
                                  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: D

                                  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 # 78
                                  A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?

                                  Answer: D

                                  Explanation:
                                  PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.


                                  NEW QUESTION # 79
                                  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 # 80
                                  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: A

                                  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 # 81
                                  What is a primary behavior that occurs during the model training stage of the AI lifecycle?

                                  Answer: B

                                  Explanation:
                                  During model training, the model learns from training data by iteratively adjusting its internal weights and biases to reduce prediction error and improve performance against the defined objective function.


                                  NEW QUESTION # 82
                                  ......

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