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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. Performance tuning and optimization
        • 2. Monitoring and observability
          Topic 2: Compute and Storage for AI Infrastructure- Storage systems
          • 1. Distributed storage concepts
            • 2. High-throughput storage for AI training data
              - Compute architecture
              • 1. HPC-style scaling considerations
                • 2. GPU-based compute nodes
                  Topic 3: 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 4: Security and Governance in AI Data Centers- Infrastructure security
                      • 1. Network segmentation and policy enforcement
                        • 2. Secure access to AI workloads
                          Topic 5: 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

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q38-Q43):

                                  NEW QUESTION # 38
                                  What describes inference traffic patterns in AI deployments?

                                  Answer: A

                                  Explanation:
                                  Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.


                                  NEW QUESTION # 39
                                  A national law firm is deploying a RAG solution for their legal research team to more efficiently use their 50 TB of confidential data and documents when preparing new contracts and briefs.
                                  They decided on a UCS X-Series chassis equipped with four GPUs and six X210c servers. Which storage solution fits this deployment?

                                  Answer: B

                                  Explanation:
                                  A RAG deployment with 50 TB of confidential documents requires scalable shared storage that can be accessed by multiple UCS X210c servers. An IP-based storage array provides centralized capacity, shared access, and operational simplicity for storing and retrieving large datasets used by the RAG pipeline.


                                  NEW QUESTION # 40
                                  Which IP-based storage transport supports flow control between directly connected devices?

                                  Answer: A

                                  Explanation:
                                  NVMe/RoCE uses RDMA over Converged Ethernet and relies on lossless Ethernet behavior, including priority-based flow control between directly connected devices. This makes it suitable for high-performance AI storage traffic that needs low latency and minimal packet loss.


                                  NEW QUESTION # 41
                                  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 # 42
                                  A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

                                  Answer: B

                                  Explanation:
                                  Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.


                                  NEW QUESTION # 43
                                  ......

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