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Free PDF Quiz 2025 The Best NVIDIA NCP-AII: Reliable NVIDIA AI Infrastructure Exam Answers

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NVIDIA AI Infrastructure Sample Questions (Q137-Q142):

NEW QUESTION # 137
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. Converting the HDF5 file to a CSV file.
B. Storing the HDF5 file on a network file system like NFS.
C. Encrypting the HDF5 file for enhanced security.
D. Reorganizing the HDF5 file to improve data contiguity and chunking.
E. Compressing the HDF5 file using gzip.

Answer: D

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 # 138
Consider a scenario where you are using NCCL (NVIDIA Collective Communications Library) for multi-GPU training across multiple servers connected via NVLink switches. Which NCCL environment variable would you use to specify the network interface to be used for communication?

A. NCCL COMM ID
B. NCCL 1B HCA
C. NCCL SOCKET IFNAME
D. NCCL NET INTERFACE
E. NCCL PORT

Answer: E

Explanation:
is the correct environment variable to specify the network interface used by NCCL. Is for Infiniband, and the other options are not directly related to specifying the network interface.

NEW QUESTION # 139
You are troubleshooting a performance issue on an Intel Xeon server with NVIDIAAI 00 GPUs. Your application involves frequent data transfers between CPU memory and GPU memory. You suspect that the PCle bus is a bottleneck. How can you verify and mitigate this bottleneck?

A. Examine the system logs for PCle errors. If there are many errors, the PCle bus is likely unstable. Mitigate by reseating the GPUs and checking the power supply.
B. Check the CPU utilization. If it's low, the PCle bus is likely the bottleneck. Mitigate by increasing the number of CPU cores assigned to the data transfer tasks.
C. Monitor the GPU temperature. If it's high, the PCle bus is likely overheating. Mitigate by improving the server's cooling.
D. Use 'nvprof to profile the application and identify the exact lines of code that are causing the high PCle traffic. Optimize those sections of code to reduce data transfers.
E. Use 'nvidia-smi' to monitor the PCle bandwidth utilization of the GPUs. If it's consistently high (near the theoretical limit), the PCle bus is likely a bottleneck. Mitigate by reducing the frequency of CPU-GPU data transfers, using pinned (page-locked) memory, and ensuring that the GPUs are connected to PCle slots with sufficient bandwidth.

Answer: D,E

Explanation:
'nvidia-smi' allows monitoring PCle bandwidth utilization, directly indicating a bottleneck. Pinned memory helps with efficient DMA transfers. Reducing transfer frequency and code optimization using 'nvprof are valid mitigation strategies. Low CPU utilization doesn't necessarily indicate PCle bottleneck. PCle errors indicate instability, not necessarily high utilization. GPU temperature is related to cooling, not directly the PCle bus being a bottleneck.

NEW QUESTION # 140
You are designing a large-scale AI training cluster spanning multiple racks. The networking topology necessitates both short-reach (within rack) and long-reach (inter-rack) connections. Which combination of cable types and transceivers is MOST cost-effective and suitable for this scenario, assuming a mix of 200GbE and 400GbE links?

A. DAC cables for all intra-rack connections, and a mix of SR4 and LR4 transceivers with multimode and single-mode fiber, respectively, for inter-rack connections.
B. DAC cables for all intra-rack connections and FR4 transceivers with single-mode fiber for inter-rack connections.
C. DAC cables for all intra-rack connections and AOC cables for all inter-rack connections.
D. Passive copper cables for all intra-rack connections and ER4 transceivers with single-mode fiber for all inter-rack connections.
E. AOC cables for all connections, both intra-rack and inter-rack.

Answer: A

Explanation:
DAC cables are cost-effective and suitable for short-reach, high-bandwidth connections within a rack. For inter-rack connections, SR4 transceivers with multimode fiber (for shorter inter-rack links) and LR4 transceivers with single-mode fiber (for longer inter-rack links) provide a good balance of cost and performance. AOCs are generally more expensive than DACs. ER4 is overkill for many inter-rack scenarios and is more expensive than LR4.

NEW QUESTION # 141
You are tasked with configuring an NVIDIAA100 GPU for a multi-tenant environment using MIG. The goal is to provide the following MIG configuration:

A. 6/0/0/0/0/0/0
B. 14/0/0/0/0/0/0
C. 1/1/1/1/1/1/1
D. 0/1 /2/3/4/5/6
E. 7/0/0/0/0/0/0

Answer: D

Explanation:
The question specifies that you need the configuration that allows 7x1g.10gb instances and one 7g.80gb. The format is num_1g/num_2g/num_3g/num_4g/num_7g/num_10g/num_20g/num_40g. In the provided configuration, 0/1/2/3/4/5/6 means one 7g, then we need the instances.

NEW QUESTION # 142
......

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