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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Security and Governance in AI Data Centers- Infrastructure security
  • 1. Secure access to AI workloads
    • 2. Network segmentation and policy enforcement
      Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
      • 1. Compute, GPU, and acceleration concepts
        • 2. Data pipeline and workload characteristics
          AI Infrastructure Deployment and Operations- Operations and lifecycle management
          • 1. Performance tuning and optimization
            • 2. Monitoring and observability
              - Deployment models
              • 1. On-premises AI clusters
                • 2. Hybrid AI infrastructure integration
                  Compute and Storage for AI Infrastructure- Compute architecture
                  • 1. GPU-based compute nodes
                    • 2. HPC-style scaling considerations
                      - 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 Nexus platform concepts
                                • 2. Cisco ACI integration concepts

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q13-Q18):

                                  NEW QUESTION # 13
                                  A customer uses Cisco AI Products within Intersight and observes packet drops for RoCEv2 traffic. Which System QoS policy configuration must be deployed to ensure that RoCEv2 traffic is classified as lossless?

                                  Answer: C

                                  Explanation:
                                  RoCEv2 traffic must be mapped to a lossless system class. Enabling the Platinum class and clearing Allow Packet Drops configures that class as lossless, preventing packet drops for RoCEv2 traffic and supporting reliable RDMA communication for AI workloads.


                                  NEW QUESTION # 14
                                  A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?

                                  Answer: C

                                  Explanation:
                                  RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.


                                  NEW QUESTION # 15
                                  A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?

                                  Answer: C

                                  Explanation:
                                  ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.


                                  NEW QUESTION # 16
                                  A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?

                                  Answer: C

                                  Explanation:
                                  PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.


                                  NEW QUESTION # 17
                                  During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

                                  Answer: B

                                  Explanation:
                                  Microbursts occur within milliseconds and are often missed by traditional monitoring methods.
                                  High-frequency streaming telemetry provides granular visibility into queue depth, drops, and latency conditions in real time. Hourly exports and infrequent manual inspection cannot reliably detect transient AI fabric congestion events.


                                  NEW QUESTION # 18
                                  ......

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