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NVIDIA AI Infrastructure Sample Questions:

1. You're setting up a cluster with 8 NVIDIA A100 GPUs. Each GPU needs to read 4GB/s from storage to keep it fully utilized. The network connecting the storage and compute nodes has a bandwidth of 25GB/s. What is the maximum number of GPUs that can be simultaneously saturated with data without exceeding the network bandwidth?

A) 4
B) 6
C) 2
D) 8
E) 5

2. You are managing a Kubernetes cluster with NVIDIA GPUs and want to automatically scale your A1 inference deployments based on GPU utilization. Which of the following tools and configurations would you use to implement Horizontal Pod Autoscaling (HPA) based on GPU metrics?

A) Manually adjusting the number of replicas in the deployment based on observed GPIJ utilization.
B) Using the standard Kubernetes HPA with CPU utilization as the scaling metric, assuming GPU utilization is correlated with CPU usage.
C) Configuring the HPA based on memory utilization.
D) Using the NVIDIA Data Center GPU Manager (DCGM) exporter to expose GPU metrics to Prometheus, and configuring the HPA to scale based on these metrics.
E) Using the Kubernetes Resource Metrics API to directly access GPU utilization metrics and configuring the HPA accordingly.

3. You are building a cloud-native application that uses both CPU and GPU resources. You want to optimize resource utilization and cost by scheduling CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs. How would you achieve this node selection and workload placement in Kubernetes?

A) Use taints and tolerations to dedicate nodes without GPUs to CPU-intensive tasks and nodes with GPUs to GPU-intensive tasks.
B) Use resource quotas to limit the CPU resources available on nodes with GPUs and the GPU resources available on nodes without GPUs.
C) Use labels to identify the CPU and GPU-intensive nodes.
D) Use node affinity rules to schedule CPU-intensive tasks on nodes with GPUs and GPU-intensive tasks on nodes without GPUs.
E) Use node affinity rules to schedule CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs.

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

5. You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod's YAML definition?

A) Specify the MIG device profile name (e.g., 'nvidia.com/mig-l g.7gb: 1 in the resource request.
B) Use a generic resource request like 'nvidia.com/gpu: 0.125' to allocate a portion of the GPIJ.
C) Using the annotation 'nvidia.com/gpu.product' and the value of the particular GPU device you desire.
D) Specify the full GPU ID (e.g., 'nvidia.com/gpu: 1') in the resource request.
E) Use a custom resource definition (CRD) to define MIG instances and reference it in the resource request.

Solutions: