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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	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 4	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 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 (Q58-Q63):

58. Frage
During cluster deployment, the UFM Cable Validation Tool reports "Wrong-neighbor" errors on multiple InfiniBand links. What is the most efficient way to resolve this issue?

A. Verify LLDP data against topology files and remediate.
B. Replace all affected cables with higher-grade OM5 fiber optics.
C. Disable FEC on all switches to bypass neighbor validation.
D. Reboot all leaf switches to force LLDP rediscovery.

Antwort: A

Begründung:
"Wrong-neighbor" errors indicate that the discovered cable peer does not match the expected topology. The efficient resolution is to compare LLDP-discovered neighbor data with the planned topology files, identify the mismatched links, and correct the cabling or topology definition accordingly.

59. Frage
A team is validating a DGX BasePOD deployment. Using cmsh, they run a command to check GPU health across all nodes. What indicates that the system is ready for AI workloads?

A. Only the head node's GPUs need to be healthy.
B. All GPUs report Status_Health = OK and Health = OK for each device.
C. The command output is ignored if the system powers on without errors.
D. At least half of the GPUs report Status_Health = OK.

Antwort: B

Begründung:
In an NVIDIA DGX BasePOD or SuperPOD environment, "Cluster Health" is a binary state: either the entire fabric and all compute resources are ready, or the cluster is considered degraded. Using the Bright Cluster Manager (BCM) shell (cmsh), administrators can aggregate telemetry from every node in the cluster. For a system to be considered "Production Ready," every single GPU across the multi-node deployment must report a status of Health = OK. This verification ensures that the hardware is communicating correctly over the PCIe bus, the NVLink fabric is initialized, and no ECC (Error Correction Code) memory errors are present. If even a single GPU in a 32-node cluster is unhealthy, collective communication libraries like NCCL may hang or experience significant performance penalties during "All-Reduce" operations, as the entire job typically scales to the speed of the slowest/unhealthiest component. Therefore, seeing Status_Health = OK for every device is the mandatory exit criterion for the bring-up phase.

60. Frage
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

A. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
B. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
C. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.
D. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
E. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.

Antwort: A,C,D,E

Begründung:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

61. Frage
An enterprise deploys NVIDIA Base Command Manager to administer a newly built AI cluster.
The infrastructure team wants to minimize manual provisioning whenever additional compute nodes are installed. Which capability of Base Command Manager most directly supports this objective?

A. Automatic retraining of machine learning models
B. Automatic migration of Kubernetes workloads between clouds
C. GPU firmware overclocking during deployment
D. Automated cluster provisioning and lifecycle management

Antwort: D

Begründung:
Base Command Manager provides centralized cluster deployment, operating system provisioning, software image management, health monitoring, and lifecycle automation. These capabilities simplify scaling AI infrastructure by reducing manual configuration effort. It does not retrain AI models, overclock hardware, or orchestrate multi-cloud workload migration.

62. Frage
An organization deploys a Kubernetes AI platform using GPU Operator. Several GPU nodes appear in the cluster, but containers requesting GPU resources remain in a Pending state. The administrator verifies that NVIDIA drivers are correctly installed. Which missing component is the most likely cause?

A. Node Feature Discovery labels
B. NVIDIA Device Plugin
C. DCGM Exporter
D. CUDA Toolkit inside user containers

Antwort: B

Begründung:
The NVIDIA Device Plugin advertises GPU resources to Kubernetes so that the scheduler can allocate GPUs to pods. Without it, GPUs remain invisible to Kubernetes even if drivers are installed correctly. DCGM Exporter provides monitoring, Node Feature Discovery adds hardware labels, and CUDA Toolkit inside containers does not expose GPU resources.

63. Frage
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