300-640 Practice Questions

GPU Utilization and GPU Performance are the two choices that directly measure the behavior of accelerator resources used by AI workloads. GPU utilization indicates how actively the accelerator's computational resources are being consumed. Persistent low utilization can expose CPU, storage, networking, synchronization, or workload-scheduling bottlenecks, while sustained high utilization generally indicates effective use of expensive GPU capacity.

Cisco Intersight is increasingly oriented toward AI-specific infrastructure visibility. Cisco states that Intersight provides actionable metrics for GPU performance , resource-utilization trends, and power efficiency. Its Metrics Explorer and monitoring capabilities provide deep telemetry for infrastructure resources, including AI accelerators.

Explorer and Monitor in options B and C represent interfaces or functional capabilities rather than the performance metrics requested by the question. System CPU Utilization is useful when determining whether a workload is host-CPU constrained, but it does not directly measure GPU performance or GPU utilization.

Accordingly, A and D are the two choices that correspond most directly to the stated objective of measuring GPU performance and utilization.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — monitoring GPU performance, utilization, resource bottlenecks, and Cisco Intersight telemetry.

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backend network

frontend network

storage network

management network

Large AI environments normally separate traffic into purpose-built fabrics because GPU collective communication, storage I/O, application access, and infrastructure management have fundamentally different performance and availability characteristics. Cisco identifies the backend network as the east-west fabric dedicated to inter-GPU communication. It carries high-bandwidth RoCEv2/RDMA traffic associated with distributed training and must provide exceptionally low latency and lossless behavior.

The frontend network provides north-south connectivity between AI nodes and the wider data center, supporting application access, inference traffic, general data movement, logging, and other in-band services. The storage network connects compute nodes to shared datasets, model checkpoints, and high-performance parallel or distributed storage systems. Cisco AI POD designs may use dedicated storage-leaf connectivity to prevent storage traffic from interfering with backend GPU collective communication. The management network serves out-of-band management interfaces for servers, switches, storage platforms, and other infrastructure components. Cisco AI reference architectures explicitly distinguish backend, frontend/storage, and management connectivity for these purposes.

Maintaining these traffic domains independently improves deterministic performance, fault isolation, congestion control, security, and operational manageability.

Study Guide Reference: AI Infrastructure Components and Architecture — network fabrics, bandwidth, latency, scalability, and AI infrastructure connectivity.

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