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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Security and Governance in AI Data Centers- Infrastructure security
  • 1. Network segmentation and policy enforcement
    • 2. Secure access to AI workloads
      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
              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
                          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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                                  Updated Cisco300-640 Exam Questions in PDF Format for Quick Preparation

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q12-Q17):

                                  NEW QUESTION # 12
                                  A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)

                                  Answer: A,E

                                  Explanation:
                                  Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.


                                  NEW QUESTION # 13
                                  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 # 14
                                  A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?

                                  Answer: B

                                  Explanation:
                                  Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.


                                  NEW QUESTION # 15
                                  An engineer configures Cisco Nexus Dashboard Fabric Controller for AI infrastructure automation. The company wants centralized policy management and simplified fabric provisioning across multiple pods. Which capability provides this benefit?

                                  Answer: A

                                  Explanation:
                                  Intent-based fabric automation simplifies deployment and policy consistency across large AI infrastructures. Nexus Dashboard Fabric Controller automates provisioning, validation, and operational management. Distributed ACLs and local VLAN switching are operational features but do not centralize orchestration. Manual CLI verification is error-prone and unsuitable for rapidly scaling AI environments.


                                  NEW QUESTION # 16
                                  A Cisco Nexus switch is configured with QoS policies for AI traffic. Which scheduling mechanism guarantees bandwidth for critical traffic classes during congestion?

                                  Answer: D

                                  Explanation:
                                  Weighted scheduling allocates bandwidth according to configured priorities, ensuring critical AI traffic receives adequate forwarding resources during congestion. FIFO provides no prioritization, while MAC filtering and port mirroring are unrelated to congestion management or QoS scheduling.


                                  NEW QUESTION # 17
                                  ......

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