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NVIDIA AI Infrastructure Sample Questions:
1. You are using GPU Direct RDMA to enable fast data transfer between GPUs across multiple servers. You are experiencing performance degradation and suspect RDMA is not working correctly. How can you verify that GPU Direct RDMA is properly enabled and functioning?
A) Examine the 'cimesg' output for any errors related to RDMA or InfiniBand drivers.
B) Use the 'ibstat command to verify that the InfiniBand interfaces are active and connected.
C) Ping the other servers to ensure network connectivity.
D) Run a bandwidth benchmark using a tool like or to measure the RDMA throughput.
E) Check the output of 'nvidia-smi topo -m' to ensure that the GPUs are connected via NVLink and have RDMA enabled.
2. 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) Using a star topology for simplified management.
B) Ensuring all servers are on the same subnet for ease of configuration.
C) Maximizing the distance between servers to improve cooling.
D) Placing servers near the network's edge to reduce latency.
E) Minimizing the number of hops between GPUs that need to communicate frequently.
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) htop
B) nvidia-smi
C) nvidia-docker
D) DCGM (Data Center GPU Manager)
E) gpustat
4. You are deploying a cloud-native AI inference service using Kubernetes and NVIDIA GPUs. You need to ensure that GPU resources are efficiently allocated and monitored. Which of the following approaches is MOST effective for achieving this within the Kubernetes environment?
A) Manually assigning specific GPU devices to pods using hostPath volumes and environment variables.
B) Deploying a dedicated monitoring agent on each node to track GPU utilization and manually adjusting pod resource requests based on these metrics.
C) Overcommitting GPU resources and relying on the Kubernetes scheduler to handle potential out-of-memory (OOM) errors.
D) Relying solely on Kubernetes' default CPU and memory resource requests and limits, assuming GPU usage will be implicitly managed.
E) Using the NVIDIA Device Plugin for Kubernetes to advertise GPU resources and utilizing resource requests and limits to schedule pods on nodes with available GPUs.
5. During the physical installation of an NVIDIA GPU, you accidentally touch the gold connector pins on the card. What is the recommended course of action BEFORE inserting the GPU into the PCle slot?
A) Clean the pins with isopropyl alcohol and a lint-free swab, ensuring they are completely dry before installation.
B) It is okay to insert it directly as is.
C) Wipe the pins with a dry cloth.
D) Blow on the pins to remove any dust or debris.
E) Use compressed air to clean the pins.
Solutions:
Question # 1 Answer: A,B,D | Question # 2 Answer: E | Question # 3 Answer: B,D | Question # 4 Answer: E | Question # 5 Answer: A |