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NCP-AII考試資訊 - NCP-AII考題

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NVIDIA NCP-AII 考試大綱：

主題	簡介
主題 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.
主題 2	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.
主題 3	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.
主題 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.
主題 5	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.

最新的 NVIDIA-Certified Professional NCP-AII 免費考試真題 (Q40-Q45):

問題 #40
A leaf switch shows "FW Version Mismatch" alerts for transceivers after cluster expansion. Which tool validates transceiver firmware against expected versions?

A. mlxconfig
B. iblinkinfo
C. flint
D. ethtool

答案：C

解題說明：
Firmware consistency is a pillar of stable InfiniBand fabric performance. When a cluster is expanded, new transceivers or cables may arrive with newer or older firmware than the existing base, leading to "FW Version Mismatch" alerts in management consoles like UFM (Unified Fabric Manager). The flint tool (or mstflint) is the correct utility for querying the specific firmware levels embedded within the transceivers. While iblinkinfo provides data on link speeds and port states, it does not provide the deep hardware-level firmware telemetry required for version validation. flint allows the administrator to query the device, compare the current burn version against the target image, and perform the necessary updates to bring the cluster into a uniform state. In NVIDIA AI infrastructure, maintaining uniform firmware across the fabric ensures that features like Adaptive Routing and Congestion Control operate predictably. Without version parity, inconsistent behavior in Forward Error Correction (FEC) or link-up negotiation can lead to intermittent performance drops that are difficult to diagnose at the application (NCCL) level.

問題 #41
A BlueField-2 DPU is being used to accelerate storage access via NVMe-oF. You notice high CPU utilization on the host server, despite the DPU. What is the MOST likely cause and the MOST effective initial troubleshooting step?

A. The host is still handling a significant portion of the 1/0 processing due to incorrect offload settings; Check the NVMe-oF initiator settings and verify that offload is enabled.
B. Incorrect RoCE configuration; Verify RoCE version and MTU settings using 'roce_util'.
C. Firmware mismatch between the host NIC driver and the BlueField DPU firmware; Upgrade both to the latest compatible versions.
D. Incorrect NVMe-oF target configuration on the storage array; Check target NQN and discovery settings.
E. Insufficient memory allocated to the BlueField DPIJ; Increase memory allocation via the BMC.

答案：A

解題說明：
The MOST likely cause of high host CPU utilization with a DPIJ accelerating NVMe-oF is that the host is still handling significant I/O processing. The initial step is to verify NVMe-oF initiator offload settings to ensure the DPIJ is actually handling the workload. Checking target configuration or RoCE, while important, wouldn't directly address high host CPU if the offload isn't working in the first place.

問題 #42
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?

A. The NGC CLI is not properly installed. Reinstall the package using 'pip install -upgrade nvidia-cli'
B. The NGC API key is incorrect or has expired. Verify the API key in your NVIDIA account and update the configuration using 'ngc config set'.
C. The NGC service is down. Check the NVIDIA NGC status page for any known outages.
D. The host does not have network access to NGC.
E. The NGC CLI configuration file is corrupted. Delete the file (A.ngc/config.json') and reconfigure the CLI.

答案：B,D,E

解題說明：
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.

問題 #43
You're troubleshooting a DGX-I server exhibiting performance degradation during a large-scale distributed training job. 'nvidia-smu shows all GPUs are detected, but one GPU consistently reports significantly lower utilization than the others. Attempts to reschedule orkloads to that GPU frequently result in CUDA errors. Which of the following is the MOST likely cause and the BEST initial roubleshooting step?

A. Insufficient cooling in the server rack; verify adequate airflow and cooling capacity for the rack.
B. A hardware fault with the GPU, potentially thermal throttling or memory issues; run 'nvidia-smi -i -q' to check temperatures, power limits, and error counts.
C. A driver issue affecting only one GPU; reinstall NVIDIA drivers completely.
D. A software bug in the training script utilizing that specific GPU's resources inefficiently; debug the training script.
E. Power supply unit (PSU) overload, causing reduced power delivery to that GPU; monitor PSU load and check PSU specifications.

答案：B

解題說明：
While all options are possibilities, the consistently lower utilization and CUDA errors point strongly to a hardware fault. Running nvidia-smi -i -q' provides detailed telemetry data, including temperature, power limits, and ECC error counts, which are crucial for diagnosing GPU hardware issues.

問題 #44
You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod's YAML definition?

A. Specify the MIG device profile name (e.g., 'nvidia.com/mig-l g.7gb: 1 in the resource request.
B. Use a custom resource definition (CRD) to define MIG instances and reference it in the resource request.
C. Specify the full GPU ID (e.g., 'nvidia.com/gpu: 1') in the resource request.
D. Using the annotation 'nvidia.com/gpu.product' and the value of the particular GPU device you desire.
E. Use a generic resource request like 'nvidia.com/gpu: 0.125' to allocate a portion of the GPIJ.

答案：A

解題說明：
When using MIG, you need to specify the MIG device profile name in the resource request to allocate a specific MIG instance to the pod. For example, 'nvidia.com/mig-1g.7gb: 1' requests one instance of the lg.7gb MIG profile. Specifying the full GPU ID will not work with MIG. Generic resource requests do not work with MIG.

問題 #45
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