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NVIDIA AI Infrastructure Sample Questions:
1. 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) Restarting the cluster periodically.
B) Implement resource quotas to limit the GPU resources that each namespace can consume.
C) Monitor GPU utilization metrics (e.g., GPU utilization, memory usage) using tools like 'nvidia-smi' or Prometheus and Grafana.
D) Increase the number of replicas in the deployment to distribute the load across more GPUs.
E) Utilize MIG (Multi-lnstance GPU) to partition GPUs and isolate workloads.
2. A data scientist reports slow data loading times when training a large language model. The data is stored in a Ceph cluster. You suspect the client-side caching is not properly configured. Which Ceph configuration parameter(s) should you investigate and potentially adjust to improve data loading performance? Select all that apply.
A) client cache size
B) fuse_client_max_background
C) mds cache size
D) client quota
3. 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) Directly in the kernel image file itself using a hex editor.
B) Passing it as an argument to bfboot during deployment.
C) In the '/etc/default/grub' file on the BlueField OS, followed by updating the GRUB configuration.
D) In the '/proc/cmdline' file. This allows immediate changes.
E) In the bootloader configuration file (e.g., extlinux.conf or grub.cfg) on the BlueFieId's flash memory.
4. 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) There is no error; MIG always creates instances regardless of resources.
B) The requested MIG configuration exceeds the GPU's available resources (e.g., compute or memory).
C) The GPU is already fully utilized by other MIG instances or processes.
D) The GPIJ is in a bad state and needs to be reset.
E) The NVIDIA driver version is outdated and does not support the requested MIG configuration.
5. Which of the following is a primary benefit of using a CLOS network topology (e.g., Spine-Leaf) in a data center?
A) Enhanced security
B) Improved scalability and bandwidth utilization
C) Reduced capital expenditure (CAPEX)
D) Increased network diameter
E) Simplified network management
Solutions:
Question # 1 Answer: B,C,E | Question # 2 Answer: A,B | Question # 3 Answer: E | Question # 4 Answer: B,C,E | Question # 5 Answer: B |