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| Section | Weight | Objectives |
|---|---|---|
| Software Stack Deployment | 25% | - NVIDIA software components
|
| Validation, Troubleshooting and Optimization | 20% | - Troubleshooting and maintenance
|
| System and Server Bring-up | 20% | - Hardware installation and validation
|
| GPU Resource Management | 15% | - GPU scheduling and optimization
|
| Networking and Storage Configuration | 20% | - NVIDIA networking solutions
|
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NEW QUESTION # 98
ClusterKit's NCCL bandwidth test shows 350 GB/s on a 400G InfiniBand fabric. How should this result be interpreted?
Answer: D
Explanation:
A 350 GB/s NCCL bandwidth result on a 400G InfiniBand fabric indicates strong end-to-end collective communication performance. This level is consistent with a healthy fabric path and effective GPUDirect RDMA operation during ClusterKit validation.
NEW QUESTION # 99
You're experiencing unexpected memory errors during the execution of a large-scale deep learning training job. Suspecting faulty memory modules, which diagnostic tool is best suited to perform a comprehensive memory test on the server's RAM?
Answer: B
Explanation:
'memtest86+' is specifically designed for performing thorough memory tests on system RAM, identifying potential errors and hardware faults. 'nvidia-smi' is for GPU monitoring, 'nvprof is a profiler, DCGM can test GPU memory, but not system RAM specifically. 'Ishw' lists hardware information, but doesn't perform diagnostic tests.
NEW QUESTION # 100
You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a 'CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?
Answer: A,E
Explanation:
A 'CUDA ERROR OUT OF MEMORY' error can occur due to insufficient shared memory within the container (A). Increasing the shared memory size allows the container to allocate more memory for inter-process communication and GPU data transfers. Monitoring GPU memory usage both inside and outside the container (D) is crucial to identify the source of the memory exhaustion. 'CUDA VISIBLE DEVICES' and (B & C) are primarily used for GPU selection and ordering, not memory management, although limiting GPU visibility could indirectly reduce overall memory consumption if the application is poorly designed and tries to allocate memory on all visible GPUs regardless of need. Lowering compute capability won't directly affect memory usage, although the application will need less memory to process, it might cause issues.
NEW QUESTION # 101
An AI training cluster with NVIDIA GPUs experiences prolonged data loading times during checkpoint reloading, causing GPUs to idle frequently, CPU utilization during data transfers remains high. Which solution most effectively optimizes storage-to-GPU throughput while reducing CPU overhead?
Answer: D
Explanation:
GPUDirect Storage enables data to move more directly between storage and GPU memory, bypassing much of the CPU-mediated data path. This reduces CPU overhead during checkpoint reloads and improves storage-to-GPU throughput, helping keep GPUs fed instead of idle.
NEW QUESTION # 102
You're designing a new InfiniBand network for a distributed deep learning workload. The workload consists of a mix of large-message all- to-all communication and small-message parameter synchronization. Considering the different traffic patterns, what routing strategy would MOST effectively minimize latency and maximize bandwidth utilization across the fabric?
Answer: C
Explanation:
A combination of AR and QOS provides the most flexible and effective solution. AR can dynamically adapt to changing traffic patterns and congestion, optimizing for large-message all-to-all communication. QOS can prioritize small-message parameter synchronization, minimizing latency for critical control traffic. Min Hop routing may not always choose the optimal paths, especially in complex topologies. Static routing is difficult to manage and doesn't adapt to changing network conditions. Disabling AR can lead to congestion.
NEW QUESTION # 103
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