NCP-AII専門知識訓練 & NCP-AII試験概要

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NVIDIA NCP-AII Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: System and Server Bring-up20%- Firmware and system configuration
  • 1. OS installation and base configuration
    • 2. BMC, BIOS, TPM and firmware updates
      - Hardware installation and validation
      • 1. Power, cooling and physical connectivity verification
        • 2. Server, GPU, network and storage components setup
          Topic 2: Networking and Storage Configuration20%- NVIDIA networking solutions
          • 1. BlueField DPU configuration
            • 2. InfiniBand and Ethernet fabric setup
              - Storage integration
              • 1. Storage performance for AI workloads
                • 2. Parallel file systems and object storage
                  Topic 3: Software Stack Deployment25%- NVIDIA software components
                  • 1. Base Command Manager and cluster management tools
                    • 2. GPU drivers, container toolkit and runtime
                      - Orchestration and workload management
                      • 1. NGC catalog and software deployment
                        • 2. Slurm, Kubernetes and container orchestration
                          Topic 4: Validation, Troubleshooting and Optimization20%- Troubleshooting and maintenance
                          • 1. Hardware and software fault isolation
                            • 2. Performance optimization and best practices
                              - Cluster validation and benchmarking
                              • 1. Health checks and error detection
                                • 2. HPL, NCCL and performance testing
                                  Topic 5: GPU Resource Management15%- GPU scheduling and optimization
                                  • 1. NVLink and fabric management
                                    • 2. Workload placement and sharing
                                      - Multi-Instance GPU (MIG) configuration
                                      • 1. Isolation and performance tuning
                                        • 2. Partitioning and resource allocation

                                          >> NCP-AII専門知識訓練 <<

                                          NVIDIA NCP-AII専門知識訓練: NVIDIA AI Infrastructure - CertShiken 高品質な製品

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                                          NVIDIA AI Infrastructure 認定 NCP-AII 試験問題 (Q106-Q111):

                                          質問 # 106
                                          You are using the NVIDIA Container Toolkit in a Kubernetes environment with multiple GPUs per node. You want to ensure that pods can request specific GPUs on a node, rather than simply requesting 'any' GPU. Which Kubernetes feature, in conjunction with the NVIDIA Device Plugin, allows you to achieve this fine-grained GPU resource allocation?

                                          正解：C

                                          解説：
                                          The Kubernetes Topology Manager (C) allows you to align resource allocations (including GPIJs) with specific NIJMA nodes. This is critical for performance when dealing with multiple GPUs per node. Resource Quotas (A) limit resource usage but don't control specific GPU selection. Node Affinity (B) selects nodes based on labels, not specific GPUs. The Device Plugins API (D) enables GPIJ discovery, but the Topology Manager is needed for fine-grained allocation within a node. Taints and Tolerations (E) are used to prevent pods from being scheduled on certain nodes unless they have the corresponding toleration, and does not directly allow for the selection of a particular GPU.


                                          質問 # 107
                                          An engineer needs to verify NVLink isolation on a single node with 8 GPUs. Which NCCL test configuration stresses switch bisection bandwidth?

                                          正解：D

                                          解説：
                                          Using all_reduce_perf across all 8 GPUs with NCCL_TESTS_SPLIT="AND 0x1" separates GPUs into traffic groups that exercise cross-switch communication paths, making it suitable for stressing NVLink switch bisection bandwidth and validating isolation behavior within the node.


                                          質問 # 108
                                          You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You've already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue?

                                          正解：B、D

                                          解説：
                                          Re-seating the GPU (D) ensures a proper connection between the GPU and the motherboard, which is crucial for effective heat dissipation. Increasing fan speed (E) can directly improve cooling. Lowering the power limit (B) reduces temperature but also reduces performance. Updating drivers (C) may help in some cases, but it is less likely to solve a thermal throttling problem. Lowering the ambient temperature (A) is generally beneficial but might not be specific enough to fix the overheating issue on a single server.


                                          質問 # 109
                                          The system administrator plans to use Multi-instance GPU profiles. What command should be used to verify that the GPU has this mode enabled?

                                          正解：C

                                          解説：
                                          nvidia-smi is the standard NVIDIA management command used to verify whether MIG mode is enabled on a supported GPU. It displays the GPU's MIG status and can also show configured MIG instances and profiles.


                                          質問 # 110
                                          An engineer needs to validate NVLink Switch functionality on a DGX H100 system with 8 GPUs. Which NCCL command verifies intra-node NVLink bandwidth?

                                          正解：B

                                          解説：
                                          The NVIDIA Collective Communications Library (NCCL) "Tests" are used to verify the maximum achievable bandwidth of the interconnects. On a DGX H100, the GPUs are connected via a dedicated high- bandwidthNVLink Switch fabric(NVLink 4), which provides significantly higher throughput than PCIe. To validate the intra-node (within a single server) performance, the all_reduce_perf test is used. The command in Option D is specifically designed to stress all 8 GPUs (-g 8) across a wide range of message sizes (8 bytes to
                                          16G). The use of the environment variable NCCL_TESTS_SPLIT with the bitwise "OR" or "AND" masks allows the engineer to isolate specific traffic patterns or groups of GPUs to ensure the NVLink switches are distributing the load evenly. For a standard 8-GPU H100 tray, achieving a "Bus Bandwidth" of ~450 GB/s to
                                          900 GB/s (depending on the precision and message size) confirms that the NVLink fabric is operating at its theoretical peak. Using only 4 GPUs (Option B) or 1 GPU (Option C) would not provide a complete picture of the NVLink switch bisection bandwidth.


                                          質問 # 111
                                          ......

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