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NEW QUESTION # 176
Your AI infrastructure includes several NVIDIAAI 00 GPUs. You notice that the GPU memory bandwidth reported by 'nvidia-smi' is significantly lower than the theoretical maximum for all GPUs. System RAM is plentiful and not being heavily utilized. What are TWO potential bottlenecks that could be causing this performance issue?
Answer: A,D
Explanation:
Inefficient data loading (B) can starve the GPUs, preventing them from reaching their full memory bandwidth potential. If the storage system or data pipeline is slow, the GPUs will spend time waiting for data. PCle Gen3 (C) has lower bandwidth than PCle Gen4, limiting the data transfer rate to the GPUs. While insufficient CPU cores (A) can be a bottleneck, it's less directly related to GPU memory bandwidth. Driver configuration (E) affects inter-GPU communication, not the memory bandwidth of individual GPUs. CPU's RAM type does not directly impact GPIJ memory bandwidth(D).
NEW QUESTION # 177
A data scientist needs to run a Jupyter notebook on specific GPU cards. What command should be used?
Answer: B
Explanation:
Docker uses the --gpus flag with a device= selector to expose only specific GPUs to a container.
The selector can target GPUs by UUID and index, allowing the Jupyter notebook container to run only on the specified GPU cards while publishing port 8888 for notebook access.
NEW QUESTION # 178
A Slurm-managed AI cluster contains both H100 and L40 GPU nodes. Several inference jobs requiring only modest GPU resources are repeatedly scheduled onto H100 nodes, delaying large distributed training jobs. Which scheduling strategy would best improve overall cluster efficiency?
Answer: A
Explanation:
Slurm supports partitions, Generic Resources (GRES), constraints, and scheduling policies that match workloads to suitable hardware. Assigning inference workloads to lower-cost GPUs while reserving H100 systems for large-scale training improves utilization and throughput. Disabling nodes wastes resources, and random scheduling ignores workload requirements.
NEW QUESTION # 179
You are designing an Ai server infrastructure using NVIDIA HGX AIOO modules. The server's power supply units (PSUs) are configured in a redundant (N+1) setup. The individual PSUs are rated for 3000W each, and the server contains three PSUs. If the expected peak power consumption of the HGX A100 modules and other components is 5500W, what is the safety margin (in Watts) in the power budget?
Answer: C
Explanation:
With N+1 redundancy and three 3000W PSUs, the total available power is 2 3000W = 6000W (N+1 means the system can tolerate one PSU failure). The safety margin is 6000W - 5500W = 500W.
NEW QUESTION # 180
You are tasked with troubleshooting a performance bottleneck in a multi-node, multi-GPU deep learning training job utilizing Horovod.
The training loss is decreasing, but the overall training time is significantly longer than expected. Which of the following monitoring approaches would provide the most insight into the cause of the bottleneck?
Answer: B
Explanation:
Horovod's timeline and profiling tools are specifically designed to visualize communication patterns and identify bottlenecks in distributed training jobs. While 'nvidia-smr and network monitoring can provide useful information, they don't give the holistic view of communication overhead that Horovod's tools provide. Loss curve analysis helps with model-related issues, not distributed training bottlenecks. 'htop' isn't related to network or GPU specific issues in distributed processing.
NEW QUESTION # 181
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