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NVIDIA AI Infrastructure Sample Questions (Q108-Q113):

NEW QUESTION # 108
You are using a custom container runtime other than Docker (e.g., containerd) and need to integrate it with the NVIDIA Container Toolkit.
What command would you use to configure the NVIDIA Container Toolkit for this runtime? (Assume your runtime configuration file is located at '/etc/containerd/config.toml')

A. nvidia-ctk runtime configure
B. nvidia-ctk runtime install -runtime-containerd'
C. nvidia-ctk runtime config -runtime=containerd -set-default'
D. nvidia-ctk runtime configure -runtime=custom -config=/etc/containerd/config.tomr
E. 'nvidia-docker runtime configure -runtime=containerd'

Answer: D

Explanation:
The Anvidia-ctk runtime configure' command is used to configure the NVIDIA Container Toolkit for different container runtimes. When the runtime is not a standard one, the '-runtime=custom' option must be used, and you also provide the path to the configuration file using config=/etc/containerd/config.tomr. There is no 'install' or 'config' subcommand under 'nvidia-ctk runtime' for runtime selection.

NEW QUESTION # 109
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. PCle Gen4 offers higher bandwidth and lower latency compared to NVLink, making it the preferred choice for all deep learning workloads.
B. NVLink and PCle Gen4 offer identical performance for inter-GPU communication; the choice is arbitrary.
C. PCle Gen4 is more cost-effective and power-efficient than NVLink, making it the optimal choice for smaller AI training datasets.
D. 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.
E. 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.

Answer: D

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 # 110
You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.
Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can't immediately improve the data center cooling. What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?

A. Increase the fan speeds of the GPU coolers to improve heat dissipation.
B. Implement a more aggressive GPU frequency scaling profile to throttle performance during peak temperatures.
C. Replace all existing GPUs with water-cooled models.
D. Reduce the GPU power limit using 'nvidia-smi' to decrease heat generation.
E. Schedule training jobs to run during off-peak hours when ambient temperatures are lower.

Answer: D,E

Explanation:
Reducing the GPIJ power limit (A) will directly reduce heat generation, which is the primary driver of failures in this scenario. While it will slightly reduce performance, the impact is less than aggressive throttling. Scheduling training jobs for off-peak hours (D) completely avoids the issue of high ambient temperatures. Increasing fan speeds (B) can help, but it may not be sufficient and can increase noise. Frequency scaling (C) is a more aggressive approach that can significantly impact performance. Replacing all GPUs with water-cooled models (E) is a more expensive and complex solution that may not be immediately feasible.

NEW QUESTION # 111
A user reports that their GPU-accelerated application is crashing with a CUDA error related to 'out of memory'. You have confirmed that the GPU has sufficient physical memory What are the likely causes and troubleshooting steps?

A. The system's virtual memory is exhausted. Increase the swap space.
B. The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.
C. The application is leaking GPU memory. Use a memory profiling tool like 'cuda-memcheck' to identify the source of the leak.
D. The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.
E. The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.

Answer: C,E

Explanation:
Memory leaks and single-allocation limits are common causes of 'out of memory' errors, even when sufficient physical memory exists. 'cuda-memcheck' is specifically designed to find memory errors in CUDA applications. While driver incompatibility is possible, leaks and allocation size limits are more frequent occurrences.

NEW QUESTION # 112
You have created MIG instances on an A100 GPU and want to dynamically adjust their size based on workload demands. Which of the following methods is the most appropriate for automatically resizing MIG instances in response to changing resource requirements?

A. Implement a script that monitors GPU utilization and automatically adjusts Kubernetes resource quotas to match.
B. Use 'nvidia-smi' to manually destroy and recreate MIG instances with different sizes as needed.
C. Leverage a GPU virtualization platform with dynamic resource allocation capabilities that integrates with MIG.
D. Adjust the application code to use less GPIJ memory dynamically.
E. Utilize CUDA MPS to dynamically allocate GPU resources to different processes.

Answer: C

Explanation:
Explanation: Dynamically resizing MIG instances requires a mechanism that can automatically adjust the underlying GPU partitioning based on workload demands. The most appropriate method is leveraging a GPU virtualization platform (C) that offers dynamic resource allocation and integrates with MIG. These platforms can monitor resource utilization and automatically resize MIG instances accordingly. Manually resizing (A) is impractical for dynamic adjustments. Kubernetes resource quotas (B) control container resource limits, not the underlying MIG configuration. CUDA MPS (D) allows sharing a single GPU but doesn't resize MIG instances. Adjusting application code (E) doesn't address the need for dynamic MIG resizing.

NEW QUESTION # 113
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