Free trial

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NVIDIA AI Infrastructure Sample Questions:

1. You encounter an error during MIG instance creation using 'nvidia-smi' stating 'Insufficient GPU resources'. Which of the following could be the cause? (Select all that apply)

A) The NVIDIA driver version is outdated and does not support the requested MIG configuration.
B) There is no error; MIG always creates instances regardless of resources.
C) The requested MIG configuration exceeds the GPU's available resources (e.g., compute or memory).
D) The GPU is already fully utilized by other MIG instances or processes.
E) The GPIJ is in a bad state and needs to be reset.

2. Consider a scenario where you are running a CUDA application on an NVIDIA GPU. The application compiles successfully but crashes during runtime with a *CUDA ERROR ILLEGAL ADDRESS* error. You've carefully reviewed your code and can't find any obvious out- of-bounds memory accesses. What advanced debugging techniques could help you pinpoint the source of this error?

A) Reduce the block size used in CUDA kernels to decrease the likelihood of shared memory conflicts.
B) Use 'cuda-memcheck' to detect memory access errors at runtime.
C) Enable ECC (Error Correction Code) memory on the GPU to detect and correct memory errors.
D) Employ the CUDA Debugger (cuda-gdb) to step through the code and inspect variable values and memory contents.
E) Utilize NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.

3. You encounter a situation where an NVIDIA driver installation fails with the error message 'ERROR: Unable to load the kernel module 'nvidia.ko'. This may be because it was built for another kernel...'. Assuming the kernel headers are correctly installed, what is the most likely cause and solution?

A) The 'nouveau' driver is still loaded. Solution: Blacklist 'nouveau- and reboot.
B) The system is running out of disk space in '/tmp'. Solution: Free up space or remount S/tmp' with more space.
C) The NVIDIA driver version is incompatible with the current kernel version. Solution: Install a compatible driver version.
D) Secure Boot is enabled, and the NVIDIA kernel module is not signed. Solution: Sign the NVIDIA kernel module with a machine owner key (MOK).
E) The DKMS module build failed. Solution: Manually rebuild the DKMS module using 'dkms autoinstall'.

4. You're monitoring the storage I/O for an AI training workload and observe high disk utilization but relatively low CPU utilization. Which of the following actions is LEAST likely to improve the performance of the training job?

A) Implementing data prefetching to load data into memory before it's needed.
B) Increasing the batch size of the training job.
C) Adding more RAM to the system.
D) Reducing the number of parallel data loading threads.
E) Switching from HDDs to NVMe SSDs.

5. You are observing high latency in your GPU-accelerated inference service deployed on Kubernetes. You suspect that GPU resource contention might be the cause. What steps can you take to diagnose and mitigate this issue within the Kubernetes environment? (Multiple Answers)

A) Utilize MIG (Multi-lnstance GPU) to partition GPUs and isolate workloads.
B) Monitor GPU utilization metrics (e.g., GPU utilization, memory usage) using tools like 'nvidia-smi' or Prometheus and Grafana.
C) Restarting the cluster periodically.
D) Implement resource quotas to limit the GPU resources that each namespace can consume.
E) Increase the number of replicas in the deployment to distribute the load across more GPUs.

Solutions: