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Cisco 300-640 Exam Syllabus Topics:

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

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

                                  NEW QUESTION # 11
                                  Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?

                                  Answer: D

                                  Explanation:
                                  A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.


                                  NEW QUESTION # 12
                                  An engineer must monitor congestion, flow latency, and traffic drops using Cisco Nexus Dashboard and chooses to use the Traffic Analytics feature. Which Nexus Dashboard feature is automatically disabled when Traffic Analytics is enabled?

                                  Answer: B

                                  Explanation:
                                  Traffic Analytics and Flow Telemetry use overlapping telemetry resources in Cisco Nexus Dashboard. When Traffic Analytics is enabled to monitor congestion, latency, and drops, Flow Telemetry is automatically disabled to avoid resource and data collection conflicts.


                                  NEW QUESTION # 13
                                  What does workload distribution offer in an AI infrastructure with local and external resources?

                                  Answer: B

                                  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 # 14
                                  Which type of Cisco Intersight profile, when deployed to fabric interconnects, includes the configuration settings for ports, VLANs, and VSANs?

                                  Answer: C

                                  Explanation:
                                  A UCS Domain profile is deployed to fabric interconnects in Cisco Intersight Managed Mode and contains the domain-level configuration, including port policies, VLANs, VSANs, network connectivity, and other fabric interconnect settings.


                                  NEW QUESTION # 15
                                  A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

                                  Answer: C

                                  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 # 16
                                  ......

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