Sibstat' verifies interface status. 'ibping' and 'ibperf are InfiniBand-specific tools for latency and bandwidth testing. NCCL (NVIDIA Collective Communications Library) is critical for distributed training, and provides valuable diagnostic information. The other options are either incomplete or rely on tools not specific to InfiniBand.

Static routing with 'ibroute' (or similar) provides the most direct and reliable way to ensure traffic follows a specific path. The SM's default algorithm might not always choose the optimal path, and QOS only prioritizes traffic, not forces a specific route. Configuring forwarding tables manually on each switch is error-prone and difficult to manage at scale.

Client-side caching in Ceph is primarily controlled by 'client cache size' which determines the amount of memory the Ceph client uses for caching data. 'mds cache size' controls the metadata server cache size, impacting metadata operations. controls the maximum number of background requests a FUSE client can make, influencing concurrency. affects the number of threads used by the OSDs, not the client-side caching, and 'client quota' limits storage usage, not caching.

Using Slurm's node features is the most straightforward and recommended approach for tagging nodes with specific capabilities. The '-constraint' option allows jobs to request nodes with particular features. GresTypeS can be used, but node features provide more flexibility and control. Installing drivers dynamically is impractical and inefficient. DCGM is primarily for monitoring, not core scheduling requirements.

The NVIDIA Device Plugin for Kubernetes (A) is essential for advertising the GPU resources to the Kubernetes scheduler. It allows Kubernetes to understand that GPUs are available and track their usage. The NVIDIA GPU Operator (C) simplifies the deployment and management of NVIDIA drivers and other components required for GPU support in Kubernetes, including the device plugin. The Resource Quota controller (B) is useful for limiting resource consumption but doesn't directly enable GPU-aware scheduling. HPA (D) is used for autoscaling based on CPU or memory utilization, not GPU utilization. Fabric Manager is for managing GPU interconnect and not related.