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NVIDIA AI Infrastructure Sample Questions (Q25-Q30):

NEW QUESTION # 25
You're working with a large dataset of microscopy images stored as individual TIFF files. The images are accessed randomly during a training job. The current storage solution is a single HDD. You're tasked with improving data loading performance. Which of the following storage optimizations would provide the GREATEST performance improvement in this specific scenario?

Answer: A

Explanation:
Random access to numerous small files is a classic use case where SSDs excel due to their low latency. Replacing the HDD with an NVMe SSD (option D) will provide the most significant performance improvement. Data deduplication (A) saves storage space but doesn't directly improve random access speed. Migrating to a sequential HDD (B) is counterproductive for random access. RAID 5 (C) provides some performance improvement but not as much as an SSD. Compression (E) can reduce storage space but adds overhead during decompression.


NEW QUESTION # 26
Why is it important to provide a large and high-performance local cache (using SSDs configured as RAID-0) for deep learning workloads on DGX systems?

Answer: C

Explanation:
Deep learning training involves iterating over a dataset many times (epochs). If a 32-node cluster pulls the same dataset from a central NFS storage server for every epoch, the network and storage fabric quickly become a bottleneck due to "Incast" traffic. By using the high-speed NVMe drives internal to a DGX system (configured inRAID-0for maximum performance, not redundancy), the system can implement a local cache.
During the first epoch, data is pulled from the remote storage and simultaneously written to the local SSDs.
For all subsequent epochs, the training framework reads the data directly from the local RAID-0 array. This significantly reduces NFS trafficand network congestion, allowing the training to proceed at the full speed of the local NVMe storage ($25 ext{ GB/s}+$ on modern DGX systems). Option C is incorrect because RAID-0 provides no redundancy; if a drive fails, the cache is lost, but since it is just a cache, the data still exists on the primary storage. Option B refers to GPUDirect Storage, which is a separate technology from local RAID-0 caching.


NEW QUESTION # 27
You're profiling the performance of a PyTorch model running on an AMD server with multiple NVIDIA GPUs. You notice significant overhead in the data loading pipeline. Which of the following strategies can help optimize data loading and improve GPU utilization?
Select all that apply.

Answer: A,B,E

Explanation:
Using multiple worker processes in 'DataLoader' enables parallel data loading. Asynchronous prefetching allows data to be loaded while the GPU is processing the current batch. A faster storage system reduces the 1/0 bottleneck. Loading the entire dataset into RAM might not be feasible for large datasets. Reducing batch size reduces the amount of data loaded but could decrease overall GPU utilizatiom


NEW QUESTION # 28
You are tasked with configuring a BlueField-2 DPIJ to offload network virtualization functions. Which Mellanox OFED command is MOST crucial for verifying that SR-IOV is correctly enabled and functional on the host and the DPU, prior to configuring vPorts?

Answer: C

Explanation:
"Ispci -vv' is the most crucial command. It shows the presence of Virtual Functions (VFs) which confirms SR-IOV is enabled. It also shows the device IDs assigned, allowing verification that the host and DPIJ are correctly seeing and assigning VFs to the network interfaces. While other commands are useful for general network diagnostics, they don't directly verify SR-IOV functionality like 'Ispci -vv' does.


NEW QUESTION # 29
You are deploying a new A1 cluster using RoCEv2 over a lossless Ethernet fabric. Which of the following QOS (Quality of Service) mechanisms is critical for ensuring reliable RDMA communication?

Answer: D

Explanation:
PFC (Priority Flow Control) is essential for RoCEv2 over Ethernet. It prevents packet loss due to congestion by pausing traffic on a specific link, ensuring reliable RDMA communication. DSCP is used for traffic prioritization but doesn't prevent loss. ECN signals congestion but relies on endpoints to react. ACLs are for security. Rate Limiting will cause unnecessary Packet Loss.


NEW QUESTION # 30
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