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NVIDIA AI Infrastructure Sample Questions:

1. You are tasked with upgrading the NVIDIA driver on a Kubernetes node hosting GPU-accelerated A1 workloads. To minimize downtime and ensure a smooth transition, which sequence of steps should you follow?

A) Delete all pods running on the node, upgrade the driver, reboot the node, and recreate the pods.
B) Upgrade the driver directly on the node, reboot the node, and let Kubernetes automatically reschedule the workloads.
C) Drain the node, upgrade the driver, reboot the node, and uncordon it.
D) Cordon the node, upgrade the driver, reboot the node, and uncordon it.
E) Upgrade the NVIDIA container toolkit, then upgrade the driver, reboot the node, and uncordon it.

2. An AI server utilizes a QSFP28 transceiver with MPO connector. During troubleshooting, you suspect a faulty transceiver. Which steps are most important to perform when physically inspecting and testing the transceiver?

A) Visually inspect the connector for damage, clean the connector end-face with an appropriate fiber optic cleaning tool, and perform an Optical Time Domain Reflectometer (OTDR) test.
B) Check the transceiver's firmware version, measure the transceiver's temperature using an infrared thermometer, and perform a bit error rate test (BERT).
C) Examine the transceiver's EEPROM data, measure the supply voltage with a multimeter, and perform a link speed auto-negotiation test.
D) Verify the transceiver's DOM (Digital Optical Monitoring) information, clean the connector end-face with a dry cloth, and perform a ping test.
E) Visually inspect the connector for damage, clean the connector end-face with an appropriate fiber optic cleaning tool, and verify the transceiver's DOM (Digital Optical Monitoring) information, particularly TX power, RX power, and temperature.

3. You are managing an AI infrastructure with multiple NVIDIAA100 GPUs configured with MIG. You want to monitor the utilization of each MIG instance individually. Which tool(s) provide granular MIG instance-level monitoring data? (Select all that apply)

A) nvidia-docker
B) DCGM (Data Center GPU Manager)
C) gpustat
D) nvidia-smi
E) htop

4. You are tasked with creating a custom Docker image for a deep learning application that requires a specific version of cuDNN. You want to minimize the image size while ensuring that the cuDNN libraries are correctly installed and configured. What is the most efficient way to achieve this?

A) Use a pre-built CUDA base image and install cuDNN during the container run.
B) Use the NVIDIA Container Toolkit to dynamically inject the cuDNN libraries into the container at runtime.
C) Download the cuDNN archive from NVIDIA, extract the libraries, and manually copy them to the appropriate locations within the Dockerfile.
D) Install the entire CUDA toolkit within the Docker image, even if only cuDNN is needed.
E) Use a multi-stage Docker build, using a base image with the desired CUDA version for building and then copying only the necessary cuDNN libraries to a smaller runtime image.

5. A user reports that their GPU-accelerated application is crashing with a CUDA error related to 'out of memory'. You have confirmed that the GPU has sufficient physical memory What are the likely causes and troubleshooting steps?

A) The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.
B) The application is leaking GPU memory. Use a memory profiling tool like 'cuda-memcheck' to identify the source of the leak.
C) The system's virtual memory is exhausted. Increase the swap space.
D) The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.
E) The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.

Solutions: