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NVIDIA AI Infrastructure Sample Questions:

1. You are managing a cluster of servers with NVIDIA GPUs. You want to automate the process of installing and updating NVIDIA drivers across all nodes. Which of the following approaches would be the most scalable and maintainable?

A) Use a configuration management tool like Ansible, Chef, or Puppet to automate the driver installation and update process.
B) Distribute the driver installation package via NFS and have each server run the installation script locally.
C) Manually SSH into each server and run the driver installation commands.
D) Create a custom script that uses 'pdsh' or 'parallel' to execute driver installation commands on all servers simultaneously.
E) Use NVIDIA's container toolkit to deploy driver containers to each node.

2. You are tasked with installing a DGX A100 server. After racking and connecting power and network cables, you power it on, but the BMC (Baseboard Management Controller) is not accessible via the network. You have verified the network cable is connected and the switch port is active. What are the MOST likely causes and initial troubleshooting steps you should take?

A) The network switch port is not configured for the correct VLAN. Verify the switch port configuration to ensure it is on the same VLAN as the BMC.
B) The BMC firmware is corrupted and needs to be reflashed using a USB drive. Check the DGX support site for the latest BMC firmware.
C) The BMC IP address is not configured or is on a different subnet. Check the BMC's network configuration using the DGX's front panel or via serial console. Verify DHCP is enabled and functioning or manually configure a static IP address.
D) The BMC is faulty and needs to be replaced. Contact NVIDIA support for RMA.
E) The BMC is not powered on because the main power supply is faulty. Verify the power supply LEDs are lit and providing power to the system.

3. An Ai infrastructure relies on a liquid cooling system to dissipate heat from multiple NVIDIA GPUs. After a recent software update, users report intermittent performance degradation and system crashes. You suspect a cooling issue. Which TWO of the following checks are the MOST critical in diagnosing the root cause?

A) Verify the pump speed and coolant flow rate within the liquid cooling system.
B) Examine the ambient temperature in the data center.
C) Analyze the system logs for GPU-related errors, specifically those indicating thermal throttling or power capping.
D) Run a memory test on the host system.
E) Check the CPU temperature using 'sensors' command.

4. Which of the following statements are true regarding the use of Congestion Management (CM) and Congestion Avoidance (CA) techniques within an InfiniBand fabric using NVIDIA technology? (Select TWO)

A) CM can include techniques like rate limiting to throttle traffic flows when congestion is detected.
B) CM/CA are not relevant in InfiniBand fabrics because InfiniBand's lossless nature guarantees that no packets will ever be dropped due to congestion.
C) InfiniBand's Explicit Congestion Notification (ECN) is a CA mechanism that allows switches to signal congestion to endpoints before packet loss occurs.
D) CM aims to reduce the severity of congestion once it has already occurred, while CA aims to prevent congestion from happening in the first place.
E) CM/CA mechanisms are primarily implemented at the IP layer and are independent of the InfiniBand transport layer.

5. You are using Docker Compose to define a multi-container application that includes a GPU-accelerated service. How would you configure the service in the 'docker-compose.ymr file to leverage the NVIDIA runtime?

A) Set the environment variable 'NVIDIA VISIBLE in the service definition.
B) Add 'deploy: resources: reservations: devices: - driver: nvidia count: all capabilities: [gpu]' to the service definition.
C) Add 'nvidia: all' to the service definition.
D) Add 'command: -gpus all' to the service definition.
E) Add 'runtime: nvidia' to the service definition.

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