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NVIDIA AI Infrastructure Sample Questions:

1. You are configuring a switch port connected to a host in an NCP-AII environment. The host is running RoCEv2. To optimize performance and prevent packet loss, which flow control mechanism should you enable on the switch port?

A) None; flow control is not needed with RoCEv2.
B) Priority Flow Control (PFC) or 802.1 Qbb, specifically for the traffic class associated with RoCEv2.
C) Simple Network Management Protocol (SNMP).
D) Spanning Tree Protocol (STP).
E) TCP flow control.

2. An AI server equipped with multiple NVIDIA GPUs experiences frequent reboots during peak workload periods. The system event logs indicate 'Uncorrectable Machine Check Exception' errors. You suspect a power delivery issue. Besides checking the PSUs, what other hardware component(s) should be thoroughly inspected to identify potential causes?

A) The CPU and system memory.
B) The network interface cards (NICs).
C) The storage drives (SSDs/HDDs).
D) The motherboard VRMs (Voltage Regulator Modules) responsible for supplying power to the GPUs.
E) The server's CMOS battery.

3. When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?

A) Ensuring all servers are on the same subnet for ease of configuration.
B) Minimizing the number of hops between GPUs that need to communicate frequently.
C) Placing servers near the network's edge to reduce latency.
D) Using a star topology for simplified management.
E) Maximizing the distance between servers to improve cooling.

4. You have an NVIDIAAIOO GPU and need to configure it for optimal performance across two distinct AI workloads: a large language model (LLM) training job and a computer vision inference service. The LLM benefits from maximum memory bandwidth, while the inference service requires low latency and high throughput. Which MIG configuration would best suit this scenario?

A) Create one log. 120gb instance for the LLM and one 4g.40gb instance for inference.
B) Utilize Time-Slicing on a single full-GPU instance, allocating specific time slots to each workload using NVIDIA Vgpu technology
C) Create two 7g.80gb MIG instances, one for each workload.
D) Create one 14g.160gb MIG instance for the LLM and use CUDA MPS to multiplex the inference service.
E) Create a single full-GPU instance and use Kubernetes resource quotas to isolate the workloads.

5. You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?

A) A hardware fault within the GPU, such as a memory error or core failure.
B) Insufficient system memory preventing data transfer to that GPU.
C) A driver bug causing incorrect workload distribution.
D) The GPU's compute mode is set to 'Exclusive Process'.
E) An incorrect CUDA version installed.

Solutions: