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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 15% | - AI Workload Characteristics - AI Infrastructure Requirements - GPU Architecture and Types - AI Ecosystem Components |
| AI Infrastructure Deployment | 25% | - Hardware Installation and Configuration - GPU Server Configuration - Storage Configuration for AI - Network Switch Configuration - Cabling and Connectivity |
| Cisco AI Infrastructure Architecture | 30% | - Storage Architecture Considerations - Network Architecture for AI Workloads - Cisco UCS AI Infrastructure Solutions - Hyperconverged Infrastructure for AI |
| AI Infrastructure Operations | 20% | - Monitoring and Management Tools - Troubleshooting Common Issues - Performance Optimization - Capacity Planning |
| AI Infrastructure Security and Compliance | 10% | - Compliance Considerations - Security Best Practices - Access Control and Authentication |
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NEW QUESTION # 62
An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?
Answer: B
Explanation:
RoCEv2 depends on low-latency and near-lossless Ethernet transport to maintain efficient RDMA communication. Packet drops trigger retransmissions and significantly degrade AI training performance. Best-effort forwarding cannot guarantee delivery quality, while fragmentation and randomized path selection do not address the primary RDMA requirement.
NEW QUESTION # 63
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?
Answer: C
Explanation:
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.
NEW QUESTION # 64
A data center team enables jumbo frames in a Cisco AI cluster. What is the primary benefit of increasing the MTU for east-west GPU traffic?
Answer: C
Explanation:
Jumbo frames reduce the number of packets needed to transmit large AI datasets, decreasing CPU interrupts and protocol overhead. This improves throughput and efficiency for GPU communication. Larger MTUs do not directly affect convergence or eliminate buffering, and they typically reduce rather than increase processing overhead.
NEW QUESTION # 65
A Cisco AI cluster requires segmentation between multiple research teams sharing the same physical infrastructure. Which solution provides scalable tenant isolation?
Answer: B
Explanation:
VXLAN overlays provide scalable multi-tenant segmentation using large VNI spaces beyond traditional VLAN limitations. AI infrastructures often support many isolated environments simultaneously. VLAN-only designs have scalability constraints, while WAN topologies and NAT do not provide modern data center tenant segmentation.
NEW QUESTION # 66
An engineer deploys an AI fabric on Cisco Nexus 9000 Series Switches connected to Cisco UCS nodes with NVIDIA GPUs. The requirements call for optimal network performance for training workloads. Adaptive routing is enabled on the NICs, and per-packet load balancing is enabled on the switches. Which other configuration must be implemented for this integration to be fully operational?
Answer: C
Explanation:
The NIC must be explicitly configured with the Cisco dynamic load-balancing setting so it recognizes that the connected Cisco Nexus fabric supports adaptive routing. This completes the integration between NIC-side adaptive routing and switch-side per-packet load balancing for optimized AI training traffic.
NEW QUESTION # 67
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