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NCP-AII試験 & NCP-AII模擬問題集

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素晴らしいNCP-AII試験試験-試験の準備方法-最高のNCP-AII模擬問題集

優れたNCP-AII試験シミュレーションを選択する方法についてまだ迷っていますか？当社JPTestKingは、長年にわたって高い合格率で有効な試験シミュレーションファイルの研究に取り組んでいます。有効なNCP-AII試験シミュレーションを見つけたい場合は、当社の製品が役立ちます。ためらうのをやめ、良い選択は、実際のテストの準備で迂回することを避けるでしょう。 NCP-AII試験のシミュレーションは、試験をクリアするのに役立ち、近い将来、国際的な企業やより良い仕事に応募できるようになります。

NVIDIA AI Infrastructure 認定 NCP-AII 試験問題 (Q210-Q215):

質問 # 210
You are troubleshooting a performance issue on an Intel Xeon server with NVIDIAAI 00 GPUs. Your application involves frequent data transfers between CPU memory and GPU memory. You suspect that the PCle bus is a bottleneck. How can you verify and mitigate this bottleneck?

A. Use 'nvprof to profile the application and identify the exact lines of code that are causing the high PCle traffic. Optimize those sections of code to reduce data transfers.
B. Monitor the GPU temperature. If it's high, the PCle bus is likely overheating. Mitigate by improving the server's cooling.
C. Examine the system logs for PCle errors. If there are many errors, the PCle bus is likely unstable. Mitigate by reseating the GPUs and checking the power supply.
D. Use 'nvidia-smi' to monitor the PCle bandwidth utilization of the GPUs. If it's consistently high (near the theoretical limit), the PCle bus is likely a bottleneck. Mitigate by reducing the frequency of CPU-GPU data transfers, using pinned (page-locked) memory, and ensuring that the GPUs are connected to PCle slots with sufficient bandwidth.
E. Check the CPU utilization. If it's low, the PCle bus is likely the bottleneck. Mitigate by increasing the number of CPU cores assigned to the data transfer tasks.

正解：A、D

解説：
'nvidia-smi' allows monitoring PCle bandwidth utilization, directly indicating a bottleneck. Pinned memory helps with efficient DMA transfers. Reducing transfer frequency and code optimization using 'nvprof are valid mitigation strategies. Low CPU utilization doesn't necessarily indicate PCle bottleneck. PCle errors indicate instability, not necessarily high utilization. GPU temperature is related to cooling, not directly the PCle bus being a bottleneck.

質問 # 211
You're deploying BlueField OS to an Arm-based SmartNIC. After flashing the image, the system fails to boot and you observe a kernel panic related to device tree loading. Which of the following is the most likely cause?

A. Insufficient memory allocated to the initrd image. This can lead to failures during initial system setup.
B. Incorrect bootloader configuration (e.g., incorrect bootargs). The bootloader might not be pointing to the correct device tree blob (dtb) or root filesystem.
C. The BlueField OS image is corrupted. A fresh download and re-flash should resolve the problem.
D. The secure boot configuration is incorrectly set up. Disabling secure boot in the BIOS or bootloader might resolve the issue.
E. The flashed image is not intended for your specific BlueField card revision. Ensure that image corresponds to hardware version.

正解：B

解説：
A device tree blob (DTB) describes the hardware to the kernel. If the bootloader doesn't point to the correct DTB or its boot arguments are incorrect, the kernel cannot properly initialize, leading to a panic. The other options could contribute to boot problems but a device tree problem commonly manifests as a kernel panic during initialization.

質問 # 212
You're optimizing a deep learning model for deployment on NVIDIA Tensor Cores. The model uses a mix of FP32 and FP16 precision. During profiling with NVIDIA Nsight Systems, you observe that the Tensor Cores are underutilized. Which of the following strategies would MOST effectively improve Tensor Core utilization?

A. Decrease the learning rate to improve training stability and reduce the need for gradient clipping.
B. Pad the input tensors to dimensions that are multiples of 8 for optimal Tensor Core alignment.
C. Enable CUDA graph capture to reduce kernel launch overhead.
D. Ensure that all matrix multiplications are performed using FP16 precision.
E. Increase the batch size to fully utilize the available GPU memory.

正解：B

解説：
Padding input tensors (C) to multiples of 8 is crucial for optimal Tensor Core performance, as Tensor Cores operate most efficiently on data with these dimensions. Using FP16 (B) is important, but proper alignment is key for full utilization. Increasing batch size (A) can improve overall throughput but doesn't directly address Tensor Core utilization. CIJDA graph capture (D) reduces kernel launch overhead, not Tensor Core utilization directly. Decreasing learning rate (E) is unrelated to Tensor Core performance.

質問 # 213
An AI server equipped with multiple NVIDIA GPUs experiences frequent reboots during peak workload periods. The system event logs indicate 'Uncorrectable Machine Check Exception' errors. You suspect a power delivery issue. Besides checking the PSUs, what other hardware component(s) should be thoroughly inspected to identify potential causes?

A. The network interface cards (NICs).
B. The storage drives (SSDs/HDDs).
C. The CPU and system memory.
D. The server's CMOS battery.
E. The motherboard VRMs (Voltage Regulator Modules) responsible for supplying power to the GPUs.

正解：E

解説：
While 'Uncorrectable Machine Check Exception' errors can have various causes, a power delivery issue to the GPUs is a strong possibility in this scenario. The motherboard VRMs are responsible for regulating and supplying power to the GPUs. If they are failing or inadequate, it can lead to power instability and these types of errors during high load.

質問 # 214
You are deploying a multi-GPU server for deep learning training. After installing the GPUs, the system boots, but 'nvidia-smi' only detects one GPU. The motherboard has multiple PCle slots, all of which are physically capable of supporting GPUs. What is the most probable cause?

A. The other GPUs are not properly seated in their PCle slots. Reseat the GPUs and ensure they are securely connected.
B. The other GPUs are faulty and need to be replaced. Test each GPU individually to confirm their functionality.
C. The system BIOS/UEFI is not configured to enable all PCle slots or the PCle lanes are not allocated correctly. Check the BIOS/IJEFI settings to enable all slots and configure the PCle lane allocation (e.g., x16/x8/x8).
D. The power supply is not providing enough power to all GPIJs. Upgrade to a higher wattage power supply.
E. The NVIDIA drivers are not installed correctly or are incompatible with the GPUs. Reinstall the drivers and ensure they are compatible with the specific GPU model and CUDA version.

正解：C

解説：
Incorrect BIOS/UEFI settings are the most likely cause when GPUs are physically present but not detected. The BIOS controls PCle lane allocation and slot enabling. Reseating GPUs is a good first step, but if the BIOS is misconfigured, it won't resolve the issue. Insufficient power is also a possibility, but BIOS configuration is more common in initial setup.

質問 # 215
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NCP-AII模擬問題集: https://www.jptestking.com/NCP-AII-exam.html

JPTestKingは専門的にNVIDIAのNCP-AII試験の最新問題と解答を提供するサイトで、NCP-AIIについての知識をほとんどカバーしています、何よりも、NVIDIAハイクラスの運用システムを備えているため、NCP-AII学習教材を使用してNCP-AII試験の準備を開始できるのは、支払い後わずか5〜10分です、NVIDIA NCP-AII試験したがって、クライアントは抽象的な概念を直感的に理解できます、我々の専門家は長年NCP-AII認定試験に従事し、そして、ヒットレートは最高99%です、レビュー段階でNCP-AII試験の準備をしているこれらの人々にとって、エラー修正は非常に重要であることがわかっています、NVIDIA NCP-AII試験もしまだ受験していないなら、はやく行動する必要がありますよ。

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NCP-AII試験の準備方法｜ハイパスレートのNCP-AII試験試験｜検証するNVIDIA AI Infrastructure模擬問題集

したがって、クライアントは抽象的な概念を直感的に理解できます、我々の専門家は長年NCP-AII認定試験に従事し、そして、ヒットレートは最高99%です、レビュー段階でNCP-AII試験の準備をしているこれらの人々にとって、エラー修正は非常に重要であることがわかっています。

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