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NEW QUESTION # 71
Your AI training pipeline involves a pre-processing step that reads data from a large HDF5 file. You notice significant delays during this step. You suspect the HDF5 file structure might be contributing to the slow read times. What optimization technique is MOST likely to improve read performance from this HDF5 file?
Answer: B
Explanation:
Reorganizing the HDF5 file (option C) to improve data contiguity and chunking is the most effective optimization. HDF5 performance is highly dependent on how the data is laid out within the file. Contiguous data and optimal chunk sizes allow for more efficient 1/0 operations. Converting to CSV (A) loses the hierarchical structure of HDF5. Storing on NFS (B) adds network overhead. Compression (D) can reduce storage space but increases decompression overhead. Encryption (E) adds overhead without improving read performance.
NEW QUESTION # 72
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?
Answer: C
Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.
NEW QUESTION # 73
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?
Answer: A,C
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 # 74
An A1 inferencing server, using NVIDIA Triton Inference Server, experiences intermittent crashes under peak load. The logs reveal CUDA out-of-memory errors (00M) despite sufficient system RAM. You suspect a GPU memory leak within one of the models. Which strategy BEST addresses this issue?
Answer: A,B
Explanation:
Options B and C directly address the 00M issue. CUDA memory pooling enables efficient reuse of GPU memory, minimizing allocations and deallocations. Reducing batch size and concurrency decreases the memory footprint of the model, alleviating the pressure on GPU memory. While upgrading GPUs (D) is a solution, it is more costly than optimizing the current configuration. Increasing system RAM (A) does not solve GPU memory issues. Disabling other models (E) reduces load but doesn't address the core problem of the memory leak in the first place.
NEW QUESTION # 75
You are designing a storage system using BeeGFS for an AI cluster. The cluster consists of 10 client nodes, each with 2 NVIDIAAIOO GPUs, and 4 storage servers. Each storage server has 10 NVMe SSDs. The training dataset is 100TB. You want to ensure high availability and performance. Which of the following BeeGFS configurations would be MOST appropriate?
Answer: B
Explanation:
Using two MDS servers in a high-availability configuration ensures that metadata operations remain available even if one MDS server fails. Having 40 OSTs (one per NVMe SSD) maximizes I/O parallelism and throughput. A single MDS server can become a bottleneck. Splitting NVMe SSDs into smaller virtual OSTs can add overhead. 10 MDS servers will add unnecessary overhead and complexity. Single OST will become bottleneck.
NEW QUESTION # 76
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