The uploaded source marks C and D . GPU Memory Utilization in option D is clearly relevant: GPU memory pressure is a critical accelerator metric because model weights, activations, KV caches, tensors, and training state consume GPU-attached memory. Cisco AI monitoring material explicitly includes GPU memory utilization among the telemetry used for AI infrastructure visibility.
Option C, Active CPU Utilization , is useful as a supporting performance metric because AI workloads can become CPU-bound during data preprocessing, tokenization, orchestration, input-pipeline execution, or host-to-device preparation. A CPU bottleneck can therefore leave GPUs underutilized even when sufficient accelerator capacity exists.
However, the question as written has a notable construction issue: Cisco documentation treats GPU utilization itself as a principal metric for evaluating accelerator utilization, but that metric is absent from the available answer choices. Cisco current material explicitly identifies GPU utilization and GPU memory utilization as AI monitoring measurements.
Therefore, CD is retained because it is the answer supplied by the source , but the technically ideal metric pair would be GPU Utilization + GPU Memory Utilization if GPU Utilization were offered.
Study Guide Reference: AI Infrastructure Operations and Troubleshooting — GPU utilization, GPU memory utilization, host-resource bottlenecks, and AI performance monitoring.
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