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NVIDIA AI Infrastructure Sample Questions (Q62-Q67):

NEW QUESTION # 62
You've installed a DGX A100 server During the initial hardware validation, you observe that one of the GPUs is consistently reporting lower performance compared to the others. Which troubleshooting steps should you take, in the CORRECT order, to diagnose the problem?

A. 1. Reseat the GPU. 2. Check GPU temperature. 3. Update the GPU driver. 4. Run GPU diagnostics.
B. 1. Update the GPU driver. 2. Run GPU diagnostics. 3. Check GPU temperature. 4. Reseat the GPIJ.
C. 1. Run GPU diagnostics. 2. Reseat the GPIJ. 3. Check GPU temperature. 4. Update the GPU driver.
D. 1. Check the power supply connections. 2. Check GPU temperature. 3. Reseat the GPIJ. 4. Run GPU diagnostics.
E. 1. Check GPU temperature. 2. Reseat the GPU. 3. Run GPU diagnostics. 4. Update the GPU driver.

Answer: E

Explanation:
Checking temperature is crucial first to avoid damaging the GPU if it's overheating. Reseating addresses potential connectivity issues. Running diagnostics identifies hardware faults. Updating the driver should be done after hardware checks to ensure the card isn't faulty.

NEW QUESTION # 63
You have configured two lg.10gb MIG instances on an NVIDIAA100 GPU. You are running a deep learning training job on one instance and want to ensure that it cannot consume resources from the other MIG instance. Which mechanism ensures isolation between the two MIG instances at the hardware level?

A. CUDA Multi-Process Service (MPS)
B. cgroups
C. Kubernetes resource quotas
D. vGPIJ scheduling
E. MIG hardware partitioning

Answer: E

Explanation:
MIG (Multi-lnstance GPU) provides hardware-level partitioning of the GPU. This means that each MIG instance has dedicated compute, memory, and memory bandwidth resources, ensuring strong isolation between the instances. CUDA MPS allows multiple processes to share a single GPU, but does not provide isolation. vGPU scheduling is for virtualized environments. Kubernetes resource quotas provide resource limits at the container orchestration level but do not provide hardware-level isolatiom Cgroups is a Linux kernel feature to limit, account, and isolate resource usage (CPU, memory, disk I/O, network, etc.) of process groups. It is often used in conjunction with containerization technologies such as Docker, but does not directly influence the MIG hardware partitioning.

NEW QUESTION # 64
You are designing an AI infrastructure using NVIDIA HGX AIOO servers. These servers support both PCle Gen4 and NVLink for GPU interconnects. Which statement is MOST accurate concerning the choice between PCle Gen4 and NVLink for inter-GPU communication within a single HGX AIOO server for deep learning training?

A. NVLink and PCle Gen4 offer identical performance for inter-GPU communication; the choice is arbitrary.
B. The choice between PCle Gen4 and NVLink depends solely on the type of deep learning framework being used; TensorFlow requires PCle Gen4, while PyTorch benefits from NVLink.
C. PCle Gen4 is more cost-effective and power-efficient than NVLink, making it the optimal choice for smaller AI training datasets.
D. PCle Gen4 offers higher bandwidth and lower latency compared to NVLink, making it the preferred choice for all deep learning workloads.
E. NVLink provides a direct, high-bandwidth, low-latency connection between GPUs, which is generally superior to PCle Gen4 for deep learning training involving frequent inter-GPU communication.

Answer: E

Explanation:
NVLink is specifically designed for high-bandwidth, low-latency communication between GPUs, making it superior to PCle Gen4 for deep learning training where GPUs frequently exchange data. NVLink allows GPIJs to share memory directly.

NEW QUESTION # 65
Which of the following are key considerations when choosing between CPU pinning and NUMA (Non-Uniform Memory Access) awareness for a distributed training job on a multi-socket AMD EPYC server with multiple GPUs?

A. CPU pinning is generally more important than NIJMA awareness because it directly impacts CPU utilization.
B. CPU pinning ensures that each process/thread runs on a specific CPU core, reducing context switching overhead. NUMA awareness ensures that the CPU cores and memory used by a process are located within the same NUMA node, minimizing memory access latency.
C. Neither CPU pinning nor NUMA awareness are relevant for GPIJ-accelerated workloads, as the GPUs handle all the computation.
D. NUMA awareness is generally more important than CPU pinning because it directly impacts memory bandwidth.
E. Both CPU pinning and NUMA awareness are critical for optimizing performance. They should be used in conjunction to achieve optimal performance.

Answer: B,E

Explanation:
CPU pinning and NUMA awareness are both important for optimizing performance on multi-socket systems. CPU pinning reduces context switching overhead, while NIJMA awareness reduces memory access latency. Using them together provides the best performance. They ensure the CPU and memory being used are on the same node, which reduces latency. While GPUs handle intense computation, CPUs prepare and feed data to the GPUs, and are critical for performance.

NEW QUESTION # 66
Consider an AI server equipped with two NVIDIAAI 00 GPUs interconnected with NVLink. You want to maximize the memory bandwidth available to a CUDA application. You observe that the application's performance doesn't scale linearly with the number of GPUs. Which of the following coding techniques or configurations could potentially improve inter-GPU memory access performance?

A. Disable NVLink to force the application to use PCle, which might provide more consistent performance.
B. Use CUDA-aware MPl for inter-GPU communication to leverage NVLink.
C. Manually manage data transfers between GPUs using 'cudaMemcpyPeer' to exploit NVLink bandwidth. Choose the GPU with more free memory for allocations.
D. Ensure all memory allocations are performed on GPU O to minimize data transfer.
E. Employ Unified Memory (I-JM) with prefetching to automatically migrate data between GPUs as needed.

Answer: C

Explanation:
ScudaMemcpyPeer allows explicit, optimized data transfers between GPUs using NVLink. Unified Memory with prefetching can simplify development, but might not always provide the best performance. CUDA-aware MPl is typically used for inter-node communication, not intra-node GPU-GPU. Allocating all memory on one GPU defeats the purpose of multi-GPU acceleration. PCle will be slower than NVLink. Manually managing memory transfers, while complex, gives the programmer the most control over leveraging NVLink bandwidth.

NEW QUESTION # 67
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