Free NCA-AIIO Practice Test Questions and Answers (2026) | Cert Empire

You are tasked with transforming a traditional data center into an AI-optimized data center using NVIDIA DPUs (Data Processing Units). One of your goals is to offload network and storage processing tasks from the CPU to the DPU to enhance performance and reduce latency. Which scenario best illustrates the advantage of using DPUs in this transformation?

A healthcare company is training a large convolutional neural network (CNN) for medical image analysis. The dataset is enormous, and training is taking longer than expected. The team needs to speed up the training process by distributing the workload across multiple GPUs and nodes. Which of the following NVIDIA solutions will help them achieve optimal performance?

You are tasked with contributing to the operations of an AI data center that requires high availability and minimal downtime. Which strategy would most effectively help maintain continuous AI operations in collaboration with the data center administrator?

You are deploying a large-scale AI model training pipeline on a cloud-based infrastructure that uses NVIDIA GPUs. During the training, you observe that the system occasionally crashes due to memory overflows on the GPUs, even though the overall GPU memory usage is below the maximum capacity. What is the most likely cause of the memory overflows, and what should youdo to mitigate this issue?

Which NVIDIA solution is specifically designed to accelerate data analytics and machine learning workloads, allowing data scientists to build and deploy models at scale using GPUs?

In your AI data center, you are responsible for deploying and managing multiple machine learning models in production. To streamline this process, you decide to implement MLOps practices with a focus on job scheduling and orchestration. Which of the following strategies is most aligned with achieving reliable and efficient model deployment?

You are managing an AI project for a healthcare application that processes large volumes of medical imaging data using deep learning models. The project requires high throughput and low latency during inference. The deployment environment is an on-premises data center equipped with NVIDIA GPUs. You need to select the most appropriate software stack to optimize the AI workload performance while ensuring scalability and ease of management. Which of the following software solutions would be the best choice to deploy your deep learning models?

Your AI data center is experiencing increased operational costs, and you suspect that inefficient GPU power usage is contributing to the problem. Which GPU monitoring metric would be most effective in assessing and optimizing power efficiency?

In an AI data center, you are working with a professional administrator to optimize the deployment of AI workloads across multiple servers. Which of the following actions would best contribute to improving the efficiency and performance of the data center?

Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?