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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions:

1. You are managing an AI data center platform that runs a mix of compute-intensive training jobs and low- latency inference tasks. Recently, the system has been experiencing unexpected slowdowns during inference tasks, even though there are sufficient GPU resources available. What is the most likely cause of this issue, and how can it be resolved?

A) The inference tasks are not optimized for the GPU architecture, leading to inefficient use of resources.
B) The inference jobs are running at the same priority level as the training jobs, causing contention for resources.
C) The training jobs are consuming too much network bandwidth, leaving insufficient bandwidth for inference data transfer.
D) The GPUs are overheating, leading to thermal throttling during inference.

2. An enterprise is deploying a large-scale AI model for real-time image recognition. They face challenges with scalability and need to ensure high availability while minimizing latency. Which combination of NVIDIA technologies would best address these needs?

A) NVIDIA Triton Inference Server and GPUDirect RDMA
B) NVIDIA CUDA and NCCL
C) NVIDIA DeepStream and NGC Container Registry
D) NVIDIA TensorRT and NVLink

3. Your team is tasked with deploying a new AI-driven application that needs to perform real-time video processing and analytics on high-resolution video streams. The application must analyze multiple video feeds simultaneously to detect and classify objects with minimal latency. Considering the processing demands, which hardware architecture would be the most suitable for this scenario?

A) Deploy GPUs to handle the video processing and analytics
B) Deploy CPUs exclusively for all video processing tasks
C) Deploy a combination of CPUs and FPGAs for video processing
D) Use CPUs for video analytics and GPUs for managing network traffic

4. In an AI infrastructure setup using NVIDIA GPUs across multiple nodes, you notice that the inter-node communication latency is higher than expected during distributed training. Which networking feature or protocol is most likely responsible for reducing latency in this scenario?

A) VLAN segmentation
B) TCP/IP over Ethernet
C) InfiniBand with RDMA (Remote Direct Memory Access)
D) Network Address Translation (NAT)

5. Which NVIDIA software component is primarily used to manage and deploy AI models in production environments, providing support for multiple frameworks and ensuring efficient inference?

A) NVIDIA NGC Catalog
B) NVIDIA Triton Inference Server
C) NVIDIA TensorRT
D) NVIDIA CUDA Toolkit

Solutions: