NCP-AII Question 142
Select 3An organization is deploying an AI workload using a cloud-native stack. The workload requires efficient GPU utilization and container orchestration. Which of the following components are essential to manage and optimize the AI infrastructure within a cloud-native environment?
- A
Kubernetes for container orchestration
- B
NVIDIA GPU Operator for GPU resource management
- C
Apache Hadoop for distributed storage
- D
Prometheus for monitoring and metrics collection
- E
NVIDIA Triton Inference Server for model serving
Show answer and explanation
Correct answers: A, B, D
Explanation
Managing a cloud-native stack for AI workloads requires components like Kubernetes for container orchestration, NVIDIA GPU Operator for GPU resource management, and Prometheus for monitoring and metrics. These tools ensure efficient deployment, utilization, and observability of AI workloads in a cloud-native environment. While tools like Hadoop or Triton may have their specific use cases, they are not essential for managing the infrastructure in this scenario.
- A. Correct.
Kubernetes is essential for managing and orchestrating containerized workloads in a cloud-native environment, including AI workloads.
- B. Correct.
NVIDIA GPU Operator simplifies the management of GPUs in Kubernetes environments, ensuring optimal GPU resource utilization for AI workloads.
- C. Incorrect.
Apache Hadoop is primarily used for distributed storage and big data processing, which is not specifically required for container orchestration or GPU management in this scenario.
- D. Correct.
Prometheus provides robust monitoring and metrics collection, which is critical for tracking the performance of AI workloads in a cloud-native environment.
- E. Incorrect.
NVIDIA Triton Inference Server is used for deploying and serving AI models, but it is not directly related to the overall management of the cloud-native infrastructure.