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NVIDIA AI Infrastructure Sample Questions:

1. You are designing a storage solution for a multi-tenant AI cluster. Different teams will be running training jobs concurrently. Which of the following considerations are MOST important for ensuring fair resource allocation and preventing performance bottlenecks?

A) Using a single large storage volume shared by all tenants
B) Isolating tenants using separate storage namespaces or volumes
C) Prioritizing I/O requests from users with higher privileges
D) Using only SSDs for all storage tiers.
E) Implementing storage quotas and quality-of-service (QOS) policies

2. You are configuring a BlueField-2 DPU for link aggregation (bonding) with two 25GbE ports. After configuring the bond interface, you notice that traffic is not being distributed across both links. What are the two most likely causes of this issue? (Select TWO)

A) The MTU size is different on the bond interface and the physical interfaces.
B) The bonding mode is set to 'balance-alb' and the ARP monitoring interval is too high.
C) The firewall on the DPU is blocking outgoing traffic on one of the interfaces.
D) The physical interfaces are not configured with the same speed and duplex settings.
E) The switch connected to the DPU does not support the bonding mode configured on the DPU.

3. Consider this scenario. You have a containerized A1 application that requires specific CUDA libraries. You want to manage the deployment and scaling of this application across multiple Kubernetes clusters, some of which might have different versions of NVIDIA drivers installed. How would you handle the CUDA dependency management in this multi-cluster environment to ensure compatibility and reproducibility?

A) Package the CUDA libraries directly into the application container image, ensuring that each cluster uses the same version.
B) Build a different container image for each Kubernetes cluster, specifically tailored to the driver version installed on that cluster.
C) Leverage an init container to install the correct CUDA libraries before the application container starts, based on the detected driver version.
D) Use a multi-stage Docker build to dynamically download and install the appropriate CUDA libraries based on the driver version detected on each cluster.
E) Use the 'latest' tag when pulling your container image to ensure the most up-to-date version is used.

4. You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough. How can you reduce the load on the CPU and improve the overall training throughput?

A) Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.
B) Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GPU.
C) Move the training data to a local NVMe drive on the training node.
D) Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.
E) Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.

5. You are implementing a distributed deep learning training setup using multiple servers connected via NVLink switches. You want to ensure optimal utilization of the NVLink interconnect. Which of the following strategies would be MOST effective in achieving this goal?

A) Configure NCCL to use GPUDirect RDMA for inter-GPU communication across servers.
B) Disable peer-to-peer GPU memory access within each server to avoid contention.
C) Increase the batch size to reduce the frequency of inter-GPU communication.
D) Implement a data compression algorithm that can be processed by the CPU before sending data over NVLink.
E) Use a standard TCP/IP socket connection for inter-GPU communication across servers.

Solutions: