NCP-AII Question 141
Select 3You are responsible for deploying and managing an AI workload in a cloud-native environment using Kubernetes and NVIDIA GPU resources. During deployment, you need to ensure that the application can efficiently access GPU resources while being compliant with cloud-native best practices. Which of the following steps should you take to manage the cloud-native stack effectively?
- A
Install the NVIDIA GPU Operator to automate GPU configuration and management in the Kubernetes cluster.
- B
Manually configure GPU drivers on each Kubernetes node to ensure compatibility with the workloads.
- C
Use a container runtime like NVIDIA Container Runtime to enable GPU acceleration for containers.
- D
Rely solely on Kubernetes' default scheduling for GPU resource allocation without any additional configuration.
- E
Leverage NVIDIA NGC (NVIDIA GPU Cloud) containers pre-optimized for AI workloads to simplify deployment.
Show answer and explanation
Correct answers: A, C, E
Explanation
Managing a cloud-native stack for AI workloads requires leveraging tools and practices that align with automation, scalability, and efficiency. The NVIDIA GPU Operator simplifies GPU configuration and management in Kubernetes clusters, while the NVIDIA Container Runtime ensures GPU acceleration for containerized applications. Pre-optimized containers from NVIDIA NGC further streamline deployment and optimize performance. Manual configurations and reliance on default scheduling contradict cloud-native principles and can lead to inefficiencies.
- A. Correct.
The NVIDIA GPU Operator is a critical component for managing GPUs in a Kubernetes cluster. It automates tasks like driver installation, runtime setup, and monitoring, which are essential for cloud-native stack management.
- B. Incorrect.
While manual configuration is possible, it is error-prone, inefficient, and against cloud-native best practices, which emphasize automation and scalability.
- C. Correct.
The NVIDIA Container Runtime is essential for enabling GPU acceleration within containers. Without it, GPU resources cannot be utilized by containerized applications properly.
- D. Incorrect.
Kubernetes' default scheduling does not natively account for GPUs, so additional configurations or tools like the NVIDIA device plugin are required to manage GPU resource allocation effectively.
- E. Correct.
NVIDIA NGC containers are pre-optimized for AI workloads and provide a cloud-native approach to deploying AI applications, reducing complexity and ensuring compatibility with NVIDIA GPUs.