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NVIDIA NCP-AII Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 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.
Thema 4	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.
Thema 5	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.

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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q183-Q188):

183. Frage
You are observing high latency in your GPU-accelerated inference service deployed on Kubernetes. You suspect that GPU resource contention might be the cause. What steps can you take to diagnose and mitigate this issue within the Kubernetes environment? (Multiple Answers)

A. Implement resource quotas to limit the GPU resources that each namespace can consume.
B. Restarting the cluster periodically.
C. Utilize MIG (Multi-lnstance GPU) to partition GPUs and isolate workloads.
D. Increase the number of replicas in the deployment to distribute the load across more GPUs.
E. Monitor GPU utilization metrics (e.g., GPU utilization, memory usage) using tools like 'nvidia-smi' or Prometheus and Grafana.

Antwort: A,C,E

Begründung:
Monitoring GPU utilization helps identify resource contention. Resource quotas prevent one namespace from consuming excessive GPU resources. MIG allows partitioning GPUs for workload isolation. Increasing replicas (option D) might help, but it doesn't address the underlying contention issue. Restarting the cluster is not a solution for ongoing contention.

184. Frage
You are deploying a multi-node A1 training cluster using Kubernetes, with each node equipped with multiple NVIDIA GPUs. You want to ensure that the Kubernetes scheduler is aware of the GPU resources available on each node and can efficiently allocate GPU-enabled pods to the appropriate nodes. Besides installing the NVIDIA Container Toolkit, what other components are essential for enabling GPU-aware scheduling in Kubernetes?

A. The NVIDIA Device Plugin for Kubernetes.
B. The Kubernetes Horizontal Pod Autoscaler (HPA).
C. The NVIDIA Fabric Manager
D. The NVIDIA GPU Operator.
E. The Kubernetes Resource Quota controller.

Antwort: A,D

Begründung:
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.

185. Frage
A data scientist reports that training performance on a DGX A100 server has significantly degraded over the past week. 'nvidia-smi' shows all GPUs functioning, but 'nvprof' reveals substantially increased 'cudaMemcpy' times. What is the MOST likely bottleneck?

A. The storage system is slow, delaying data loading and preprocessing.
B. The NVLink connections between GPUs are failing, forcing data transfers through PCle.
C. The CPU is heavily loaded, causing contention for system memory bandwidth.
D. The GPUs are overheating, causing thermal throttling and slower memory transfers.
E. The PCle bus is saturated, limiting data transfer speeds between the CPU and GPUs.

Antwort: C

Begründung:
Increased ScudaMemcpy' times indicate a bottleneck in data transfer between the CPU and GPIJs or within the GPU memory itself. While PCle saturation or failing NVLink connections could contribute, a heavily loaded CPU is often the primary culprit. CPU-bound preprocessing tasks or general system load can create contention for system memory, slowing down data movement to the GPUs. The NVLink being used is less likely because 'nvidia-smi' would likely report errors or lower speeds between the GPUs

186. Frage
You have a BlueField-2 DPU running Ubuntu. After upgrading the MLNX OFED drivers, the DPU fails to boot properly. You are presented with a GRUB prompt. Which sequence of commands is most likely to help you boot into a working kernel?

Antwort: B

Begründung:
When a boot fails after a driver upgrade, it's often because the new kernel/initrd is faulty. The most likely solution is to boot into the previous , known-good kernel. Option C attempts this by specifying '-old' in the kernel and initrd filenames, attempting to load the older, hopefully functional, kernel. It assumes the root partition is /dev/sda2 but is booting an older kernel. Option A uses '$(uname -r)' which likely resolves to the problematic kernel that caused the failure. Option B lacks version information and may also load a broken kernel. Option D uses /dev/nvme0n1p2, which might be incorrect for the root partition on the BlueField. Option E will try to load grub normally, which might fail again.

187. Frage
ClusterKit's NCCL bandwidth test shows 350 GB/s on a 400G InfiniBand fabric. How should this result be interpreted?

A. Critical failure; expected is greater than 390 GB/s for HDR InfiniBand.
B. Inconclusive; rerun with --stress=cpu to validate.
C. Suboptimal performance; requires FEC tuning to reach 380+ GB/s.
D. Optimal performance, indicating healthy fabric and GPUDirect RDMA.

Antwort: D

Begründung:
The result should be interpreted as optimal in the context of the provided ClusterKit NCCL bandwidth test.
ClusterKit is designed for high-performance cluster validation and includes GPU communication tests such as GPU-GPU latency, GPU-GPU bandwidth, GPU-host latency, and NCCL bandwidth and latency. A reported
350 GB/s value in this type of question normally represents aggregate NCCL communication bandwidth across GPUs, not the raw line rate of a single 400G network port. If ClusterKit reports this result as part of a healthy NCCL bandwidth run, it indicates that the GPU communication path, RDMA stack, and fabric are performing as expected. Option A is incorrect because it incorrectly references HDR and assumes a single- link expectation above 390 GB/s. Option B incorrectly jumps to FEC tuning without evidence of link errors, retransmissions, or degraded counters. Option D is also incorrect because CPU stress testing does not validate NCCL GPU-to-GPU fabric bandwidth. In production validation, this result should still be considered alongside consistency, error counters, NCCL logs, topology, and reference values for the exact DGX, GPU, HCA, and switch configuration.

188. Frage
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