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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD
  • Intel servers and storage.
Topic 2
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3
  • System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC
  • OOB
  • TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 4
  • Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 5
  • Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm
  • Enroot
  • Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.

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NVIDIA AI Infrastructure Sample Questions (Q61-Q66):

NEW QUESTION # 61
An administrator needs to add additional GPUs to an existing server. What are the server requirements to check before installing new GPUs?

Answer: B

Explanation:
The correct answer is D because adding GPUs to an existing server requires validation of physical, electrical, thermal, and platform compatibility requirements before installation. The server must have available PCIe slot allocation with the correct mechanical size, electrical lane support, and platform topology so the GPU can operate at the expected bandwidth. It must also have compatible hardware, including supported risers, power cables, firmware, BIOS settings, chassis airflow design, and driver support. Adequate rack power is mandatory because modern NVIDIA data center GPUs can significantly increase node and rack power draw under AI workloads. NVIDIA DGX SuperPOD data center guidance emphasizes that DGX rack density must fit within available power and cooling capacity, and NVIDIA-Certified Systems guidance notes that optimal PCIe server configuration depends on workload and system design. Cooling is equally critical because insufficient airflow or data center cooling can cause thermal throttling, instability, or hardware shutdowns.
Storage and networking are important for workload design, but they are not the core server installation requirements for physically adding GPUs.


NEW QUESTION # 62
A system administrator receives an alert about a potential hardware fault on an NVIDIA DGX A100. The GPU performance seems degraded, and the system fans are operating loudly. What step should be recommended to identify and troubleshoot the hardware fault?

Answer: C

Explanation:
nvidia-smi is the appropriate first diagnostic tool for checking GPU health, utilization, power, temperature, throttling, and error indicators. Since degraded performance and loud fans may point to thermal or GPU hardware issues, reviewing GPU status and temperatures helps identify the fault condition before taking corrective action.


NEW QUESTION # 63
A system administrator needs to enable MIG so that the end user can run multiple jobs on an NVIDIA A100 GPU. What command should be used?

Answer: C

Explanation:
nvidia-smi -i 0 -mig 1 enables MIG mode on GPU index 0. Once MIG mode is enabled, the A100 can be partitioned into multiple GPU instances so separate jobs can run with isolated GPU resources.


NEW QUESTION # 64
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?

Answer: B

Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn't directly address memory allocation issues. Therefore it will have less impact than others.


NEW QUESTION # 65
You are configuring a BlueField-3 DPLJ for a cloud-native application using Kubernetes. You want to offload container networking using OVS (Open vSwitch). Which of the following configuration steps are NECESSARY to integrate the BlueField-3 DPIJ with the Kubernetes cluster for network offload? (Select TWO)

Answer: C,D

Explanation:
The NVIDIA BlueField Kubernetes Operator is essential for automating the management and configuration of the DPIJ within the Kubernetes environment. This includes creating and managing OVS bridges. Integrating the Kubernetes CNI to use the OVS bridge managed by the BlueField DPIJ allows pod networking traffic to be offloaded to the DPU. Installing Mellanox OFED everywhere isn't needed with the operator. While you could manually create the bridges (E), the operator is the preferred method. The DPIJ acting as a DHCP server (D) is not a requirement for simple network offload.


NEW QUESTION # 66
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