NCP-AIN 無料問題集「NVIDIA-Certified Professional AI Networking」

（A）MLNX_OFED

（B）NVIDIA Container Runtime

（C）NVIDIA GPU Cloud

（D）CUDA Toolkit

（A）Role-Based Access Control (RBAC)

（B）Network Access Control (NAC)

（C）Virtual Network Segmentation (VNS)

（D）Digital Certification Management (DCM)

（A）When investigating reports of network congestion or latency problems.

（B）When optimizing job placement and workload distribution across the cluster.

（C）After making changes to network configuration.

（D）When troubleshooting a single node's hardware failure.

（A）By analyzing the egress queue loads ensuring all ports are well-balanced.

（B）By measuring the number of connected devices in the network.

（C）By assessing the physical distance between network devices.

（D）By monitoring the CPU and power usage of network devices.

（A）By managing GPU utilization

（B）By providing the necessary high-bandwidth, low-latency infrastructure for fast data transfer

（C）By generating synthetic training data

（D）By providing data encryption and privacy for model training

（A）Random Routing

（B）Round Robin Routing

（C）Adaptive Routing

（D）Shortest Path Routing

（A）By ensuring end-to-end encryption of AI data

（B）By hosting machine learning frameworks

（C）By providing a fast interconnect for training data across multiple GPUs

（D）By managing deep learning model deployment

（A）RDMA Shared Device Plugin

（B）Secondary Network

（C）NV IPAM

（D）OFED Driver

（A）SHARP v2

（B）Graph Search Optimization

（C）PXN

（D）Adaptive Routing