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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Compute and Storage for AI Infrastructure- Compute architecture
  • 1. GPU-based compute nodes
    • 2. HPC-style scaling considerations
      - Storage systems
      • 1. Distributed storage concepts
        • 2. High-throughput storage for AI training data
          Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
          • 1. Data pipeline and workload characteristics
            • 2. Compute, GPU, and acceleration concepts
              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
                      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
                              Security and Governance in AI Data Centers- Infrastructure security
                              • 1. Secure access to AI workloads
                                • 2. Network segmentation and policy enforcement

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q11-Q16):

                                  NEW QUESTION # 11
                                  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: B

                                  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 # 12
                                  What does workload distribution offer in an AI infrastructure with local and external resources?

                                  Answer: D

                                  Explanation:
                                  Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.


                                  NEW QUESTION # 13
                                  A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
                                  - high availability of the chassis management plane.
                                  - support for up to 768 GB of memory for in-memory model storage and
                                  processing
                                  - remote server launch requiring no on-site IT staff
                                  - use of Cisco Intersight for SaaS-based infrastructure lifecycle
                                  management
                                  Which solution meets the requirements?

                                  Answer: A

                                  Explanation:
                                  Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.


                                  NEW QUESTION # 14
                                  A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?

                                  Answer: B

                                  Explanation:
                                  A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.


                                  NEW QUESTION # 15
                                  What is a primary behavior that occurs during the model training stage of the AI lifecycle?

                                  Answer: D

                                  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 # 16
                                  ......

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