100% Pass Quiz Updated Cisco - 300-640 - Implementing Cisco Data Center AI Infrastructure Accurate Test

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Cisco 300-640 Exam Syllabus Topics:

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

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q76-Q81):

                                  NEW QUESTION # 76
                                  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 # 77
                                  An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?

                                  Answer: D


                                  NEW QUESTION # 78
                                  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 # 79
                                  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: C

                                  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 # 80
                                  Which result is provided through image recognition using AI?

                                  Answer: C

                                  Explanation:
                                  Image recognition uses AI to analyze digital images or video frames and identify, detect, classify, or label visual elements such as objects, people, patterns, or scenes.


                                  NEW QUESTION # 81
                                  ......

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