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NVIDIA AI Infrastructure Sample Questions:

1. You've installed the NGC CLI, but when you run 'ngc registry model list' you get an error indicating authentication failure. You're sure your API key is correct. What could be the cause, and how would you diagnose this?

A) The host machine's clock is not synchronized, causing authentication issues. Synchronize the clock using 'ntpd' or 'chronyd'.
B) Your account lacks the necessary permissions to access the NGC registry. Contact your NVIDIA administrator.
C) The NGC CLI version is outdated. Upgrade to the latest version using 'pip install -upgrade nvidia-cli'.
D) The environment variables 'NGC API_KEY or 'NGC CLI_API_KEY are set incorrectly or not set at all. Verify and set them correctly.
E) Your organization might be behind a proxy that is blocking the NGC CLI from accessing the internet. Configure the proxy settings for the NGC CLI.

2. A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?

A) A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.
B) The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
C) The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
D) A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.
E) There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.

3. 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?

A) CPU pinning is generally more important than NIJMA awareness because it directly impacts CPU utilization.
B) NUMA awareness is generally more important than CPU pinning because it directly impacts memory bandwidth.
C) CPU pinning ensures that each process/thread runs on a specific CPU core, reducing context switching overhead. NUMA awareness ensures that the CPU cores and memory used by a process are located within the same NUMA node, minimizing memory access latency.
D) Neither CPU pinning nor NUMA awareness are relevant for GPIJ-accelerated workloads, as the GPUs handle all the computation.
E) Both CPU pinning and NUMA awareness are critical for optimizing performance. They should be used in conjunction to achieve optimal performance.

4. 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?

A) Compressing the TIFF files using a lossless compression algorithm.
B) Replacing the HDD with a single NVMe SS
C) Implementing data deduplication on the storage volume.
D) Migrating the data to a large, sequential HDD.
E) Replacing the HDD with a RAID 5 array of HDDs.

5. You've flashed the BlueField OS to your SmartNlC, but you need to customize the kernel command line arguments (bootargs) to enable a specific feature. Where is the MOST appropriate place to modify these arguments for persistent changes that survive reboots?

A) In the '/proc/cmdline' file. This allows immediate changes.
B) Directly in the kernel image file itself using a hex editor.
C) In the '/etc/default/grub' file on the BlueField OS, followed by updating the GRUB configuration.
D) Passing it as an argument to bfboot during deployment.
E) In the bootloader configuration file (e.g., extlinux.conf or grub.cfg) on the BlueFieId's flash memory.

Solutions: