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NVIDIA AI Infrastructure Sample Questions:

1. You have a Kubernetes cluster with nodes running different versions of the NVIDIA driver. You need to ensure that your containerized AI applications are always compatible with the specific driver version running on the node where they are scheduled. How can you achieve this driver version compatibility in a cloud-native way?

A) Use a shared volume to mount drivers into a container.
B) Implement a webhook that inspects the node labels and injects the appropriate NVIDIA libraries into the pod at runtime.
C) Use the NVIDIA driver capabilities to detect the driver version at runtime and dynamically load the correct libraries.
D) Manually create different container images for each driver version and use node selectors to schedule the correct image on the appropriate nodes.
E) Use the NVIDIA Operator to automatically manage driver installations and updates on the nodes, ensuring a consistent driver version across the cluster.

2. You are configuring a server with multiple GPUs for CUDA-aware MPI. Which environment variable is critical for ensuring proper GPU affinity, so that each MPI process uses the correct GPU?

A) LD LIBRARY PATH
B) CUDA DEVICE ORDER
C) CUDA VISIBLE DEVICES
D) CUDA LAUNCH BLOCKING-I
E) MPI GPU SUPPORT

3. You are implementing a security policy on a BlueField-2 DPU to filter traffic based on specific application signatures. Which technology, supported by BlueField, allows you to achieve deep packet inspection (DPI) and apply security rules based on the detected application?

A) OVS (Open vSwitch) with OpenFlow rules.
B) TC (Traffic Control) with 'iptables' rules.
C) IPsec (Internet Protocol Security) tunnels.
D) Netfilter with connection tracking.
E) eBPF (extended Berkeley Packet Filter) with XDP (eXpress Data Path).

4. You have created MIG instances on an A100 GPU and want to dynamically adjust their size based on workload demands. Which of the following methods is the most appropriate for automatically resizing MIG instances in response to changing resource requirements?

A) Use 'nvidia-smi' to manually destroy and recreate MIG instances with different sizes as needed.
B) Implement a script that monitors GPU utilization and automatically adjusts Kubernetes resource quotas to match.
C) Leverage a GPU virtualization platform with dynamic resource allocation capabilities that integrates with MIG.
D) Adjust the application code to use less GPIJ memory dynamically.
E) Utilize CUDA MPS to dynamically allocate GPU resources to different processes.

5. You are designing an AI infrastructure using NVIDIA HGX AIOO servers. These servers support both PCle Gen4 and NVLink for GPU interconnects. Which statement is MOST accurate concerning the choice between PCle Gen4 and NVLink for inter-GPU communication within a single HGX AIOO server for deep learning training?

A) NVLink and PCle Gen4 offer identical performance for inter-GPU communication; the choice is arbitrary.
B) NVLink provides a direct, high-bandwidth, low-latency connection between GPUs, which is generally superior to PCle Gen4 for deep learning training involving frequent inter-GPU communication.
C) The choice between PCle Gen4 and NVLink depends solely on the type of deep learning framework being used; TensorFlow requires PCle Gen4, while PyTorch benefits from NVLink.
D) PCle Gen4 is more cost-effective and power-efficient than NVLink, making it the optimal choice for smaller AI training datasets.
E) PCle Gen4 offers higher bandwidth and lower latency compared to NVLink, making it the preferred choice for all deep learning workloads.

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