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NVIDIA AI Infrastructure Sample Questions (Q114-Q119):

NEW QUESTION # 114
You are tasked with designing a high-performance network for a large-scale recommendation system. The system requires low latency and high throughput for both training and inference. Which interconnect technology is MOST suitable for connecting the nodes within the cluster?

A. 10 Gigabit Ethernet
B. Fibre Channel
C. 100 Gigabit Ethernet
D. InfiniBand
E. Gigabit Ethernet

Answer: D

Explanation:
InfiniBand is designed for high-performance computing and offers significantly lower latency and higher bandwidth compared to Ethernet or Fibre Channel, making it the most suitable choice for demanding workloads like recommendation systems. While 100 Gigabit Ethernet provides high bandwidth, InfiniBand generally offers lower latency.

NEW QUESTION # 115
You are designing a storage solution for a new AI inference cluster that requires extremely low latency for model serving. Which storage technology and configuration would be MOST suitable to meet this stringent latency requirement?

A. A distributed file system deployed on spinning HDDs with a large read-ahead cache.
B. A software-defined storage (SDS) solution running on commodity hardware with SATA SSDs.
C. Amazon S3 object storage accessed over a high-bandwidth internet connection.
D. NVMe-oF (NVMe over Fabrics) using RDMA over Converged Ethernet (RoCE) connected to a cluster of NVMe drives.
E. A traditional Fibre Channel SAN with a dedicated storage array.

Answer: D

Explanation:
NVMe-oF with RoCE (option B) provides the lowest latency due to the combination of NVMe's low-latency access to flash storage and RDMA's ability to bypass the CPU for data transfer. HDDs (A) are too slow. SATA SSDs (C) are slower than NVMe. Object storage (D) introduces network latency. Fibre Channel (E) is a viable option, but NVMe-oF typically offers lower latency and greater flexibility.

NEW QUESTION # 116
An AI infrastructure utilizes NVIDIA ConnectX-7 NICs for inter-node communication. The requirement is to achieve a bandwidth of 400GbE with low latency over a distance of 100 meters. Which transceiver and cable type combination is MOST suitable for this scenario?

A. QSFP-DD DR4 transceiver with single-mode fiber cable.
B. QSFP-DD SR4 transceiver with OM4 multi-mode fiber cable.
C. QSFP-DD SR8 transceiver with OM3 multi-mode fiber cable.
D. QSFP-DD LR8 transceiver with OM4 multi-mode fiber cable.
E. QSFP-DD FR4 transceiver with single-mode fiber cable.

Answer: B

Explanation:
QSFP-DD SR4 with OM4 provides 400GbE over short distances (up to 100m) using multi-mode fiber. DR4 and FR4 require single- mode fiber and are typically used for longer distances. LR8 typically requires single mode fibre for specified distances. Using SR8 with OM3 is unlikely to achieve 400GbE as SR8 works best with OM4/OM5.

NEW QUESTION # 117
You are setting up network fabric ports for hosts in an NVIDIA-Certified Professional A1 Infrastructure (NCP-AII) environment. You need to configure Jumbo Frames to improve network throughput. What is the typical MTU (Maximum Transmission Unit) size you would set on the network interfaces and switches, and why?

A. 9000 bytes, also known as Jumbo Frames, reduces overhead and improves throughput for large data transfers.
B. 576 bytes, the minimum MTU size required by IPv4.
C. Any MTU size between 1500 and 9000 bytes; the specific value doesn't matter.
D. 1500 bytes, as it's the default and compatible with most networks.
E. 65535 bytes, the theoretical maximum MTU size, for maximum performance.

Answer: A

Explanation:
For optimal performance in an A1 training environment, Jumbo Frames (MTU of 9000 bytes) are recommended. Increasing the MTU reduces the number of packets required to transmit a given amount of data, thereby reducing processing overhead and improving overall throughput. While technically larger MTUs are possible, 9000 is the standard for Jumbo Frames.

NEW QUESTION # 118
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?

A. Reorganizing the HDF5 file to improve data contiguity and chunking.
B. Compressing the HDF5 file using gzip.
C. Storing the HDF5 file on a network file system like NFS.
D. Converting the HDF5 file to a CSV file.
E. Encrypting the HDF5 file for enhanced security.

Answer: A

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 # 119
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