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NVIDIA AI Infrastructure Sample Questions:

1. You are developing a CI/CD pipeline for deploying A1 models to a Kubernetes cluster with NVIDIA GPUs. Your pipeline includes steps to build, test, and deploy the model as a containerized application. What is the recommended approach for ensuring that the application container image includes the correct NVIDIA drivers and libraries?

A) Manually installing the NVIDIA drivers and libraries inside the container image during the build process.
B) Using a base image provided by NVIDIA that includes the necessary drivers and libraries.
C) Relying on the NVIDIA Container Toolkit to automatically install the drivers and libraries at runtime.
D) Configuring the cluster to download and configure the needed drivers just before deploying your application.
E) Downloading the latest drivers from NVIDIA's website and adding them to the container image.

2. You are managing an NVIDIA DGX A100 server and notice that GPU utilization fluctuates significantly during a supposedly constant training workload. You suspect power capping might be the cause. How can you definitively determine if power capping is active and affecting GPU performance?

A) Monitor GPU temperature using 'nvidia-smi'. If the temperature is consistently below the thermal threshold, power capping is likely active.
B) Use 'nvidia-smi' to query the 'ClocksThrottleReasonS. If 'PowerCap' is listed as active, power capping is in effect.
C) Monitor the voltage supplied to the GPUs. If the voltage is consistently lower than the maximum rated voltage, power capping is active.
D) Check the server's BIOS settings for any power management configurations that might be limiting GPU power consumption.
E) Examine the 'pstate' value reported by 'nvidia-smi'. A lower 'pstate' indicates power capping.

3. Which command-line tool is typically used to monitor the status and performance of an NVIDIA NVLink Switch?

A) nvswitch-cli
B) nvidia-smi
C) Ispci
D) rocminfo
E) ibstat

4. Consider a scenario where you are setting up a high-performance computing cluster with several GPU-accelerated nodes using Slurm as the resource manager. You want to ensure that jobs requesting GPUs are only scheduled on nodes with the appropriate NVIDIA drivers and CUDA toolkit installed. How can you achieve this within Slurm?

A) Create a custom Slurm script that checks for the presence of the NVIDIA driver and CUDA toolkit before submitting a job to a node. If the requirements are not met, the job is rejected.
B) Use Slurm's node features to tag nodes with the "Feature=' keyword in 'slurm.conf. For example, tag nodes with GPUs as 'Feature=gpu' . Jobs can then request nodes with the 'gpu' feature using the option.
C) Utilize Slurm's Prolog and Epilog scripts to dynamically install the necessary NVIDIA drivers and CUDA toolkit on each node before and after a job runs. This ensures that the required software is always available.
D) Install the NVIDIA Data Center GPU Manager (DCGM) on each node and configure Slurm to query DCGM for GPU availability and health. Slurm will then only schedule jobs on healthy and available GPUs.
E) Use Slurm's 'GresTypeS configuration option in 'slurm.conf to define a generic resource type called 'gpu' and then configure each node to advertise the available GPIJs. Slurm will automatically ensure that jobs requesting GPUs are only scheduled on nodes with the 'gpu' resource.

5. You need to remotely monitor the GPU temperature and utilization of a server without installing any additional software on the server itself. Assuming you have network access to the server's BMC (Baseboard Management Controller), which protocol and standard data format would BEST facilitate this?

A) SNMP (Simple Network Management Protocol) with MIB (Management Information Base)
B) HTTP with JSON
C) SSH with plain text output from 'nvidia-smi'
D) Syslog with CSV (Comma-separated Values)
E) IPMI (Intelligent Platform Management Interface) with SDR (Sensor Data Records)

Solutions: