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NVIDIA AI Infrastructure Sample Questions:

1. You observe the following output from 'nvidia-smi' on a server running a large Ai training job:

What does the 'ClocksThrottleReasons.ThermalSlowdown: Active' indicate, and what immediate action should you take?

A) The GPU's power draw is exceeding its thermal design power (TDP). Reconfigure the power supply.
B) The GPU driver is outdated and needs to be updated to improve thermal management.
C) The ambient temperature in the server room is too low, causing the GPU to underperform.
D) The GPU is operating at its maximum power limit, and no action is needed.
E) The GPU's clock speeds are being reduced to prevent overheating. Investigate the cooling system and reduce the GPU workload.

2. You notice that one of the fans in your GPU server is running at a significantly higher RPM than the others, even under minimal load. ipmitool sensor' output shows a normal temperature for that GPU. What could be the potential causes?

A) The fan's PWM control signal is malfunctioning, causing it to run at full speed.
B) A network connectivity issue is causing higher CPU utilization, leading to increased system-wide heat.
C) The fan is attempting to compensate for restricted airflow due to dust buildup.
D) The server's BMC (Baseboard Management Controller) has a faulty temperature sensor reading, causing it to overcompensate.
E) The fan bearing is wearing out, causing increased friction and requiring higher RPM to maintain airflow.

3. You've successfully deployed BlueField OS to your SmartNlC. You need to verify that the Mellanox Ethernet driver (mlx5) is loaded and functioning correctly. What command would you use to confirm this?

A)

B)

C)

D)

E)

4. Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?

A) Using namespaces with resource quotas and network policies.
B) Using node affinity only.
C) Using labels and selectors to schedule workloads on specific GPU nodes.
D) Using taints and tolerations to dedicate GPU nodes to specific workloads.
E) Using pod affinity and anti-affinity rules to control pod placement.

5. Consider a scenario where you're using GPUDirect Storage to enable direct memory access between GPUs and NVMe drives. You observe that while GPUDirect Storage is enabled, you're not seeing the expected performance gains. What are potential reasons and configurations you should check to ensure optimal GPUDirect Storage performance? Select all that apply.

A) Disable CPU-side caching to force all I/O operations to go directly to the GPU memory.
B) Check if the file system supports direct I/O (e.g., using 'directio' mount option).
C) Verify that the NVMe drives are properly configured in a RAID 0 configuration.
D) Ensure that the NVMe drives are connected to the system via PCle Gen4 or Gen5.
E) Confirm that the CUDA driver version is compatible with GPIJDirect Storage.

Solutions: