NCA-AIIO Practice Test Questions

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If you're planning to earn the NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) certification, there's no better way to prepare than by practicing with the most updated AI Infrastructure and Operations (NCA-AIIO) Exam Questions from PassQuestion.

Which of the following is a primary challenge when integrating AI into existing IT infrastructure? A. Scalability of the AI workloads. B. Ensuring AI models have a user-friendly interface. C. Finding AI tools that are compatible with existing hardware. D. Selecting the right cloud service provider.

Answer: A

You are managing an AI infrastructure setup where multiple GPUs are used to accelerate deep learning workloads. Suddenly, one of the nodes in the GPU cluster becomes unresponsive, leading to a significant drop in training performance. What should be your first course of action to troubleshoot the issue? A. Check the network connectivity of the node. B. Restart the entire GPU cluster. C. Reconfigure the AI model to use fewer GPUs. D. Update the drivers for all GPUs in the cluster.

Answer: A

You are supporting a senior engineer in troubleshooting an AI workload that involves real-time data processing on an NVIDIA GPU cluster. The system experiences occasional slowdowns during data ingestion, affecting the overall performance of the AI model. Which approach would be most effective in diagnosing the cause of the data ingestion slowdown? A. Profile the I/O operations on the storage system. B. Optimize the AI model's inference code. C. Switch to a different data preprocessing framework. D. Increase the number of GPUs used for data processing.

Answer: A

In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you're considering several strategies. Which of the following would most effectively balance energy efficiency with maintaining performance? A. Disabling all energy-saving features to ensure maximum performance B. Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds C. Running all GPUs at the lowest possible clock speeds D. Enabling deep sleep mode on all GPUs during processing times

Answer: B

When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)? A. Increase the CPU allocation to each VM. B. Ensure that GPU memory is not overcommitted among VMs. C. Enable nested virtualization on the VMs. D. Allocate more network bandwidth to the host machine.

Answer: B

Your AI infrastructure team is observing out-of-memory (OOM) errors during the execution of large deep learning models on NVIDIA GPUs. To prevent these errors and optimize model performance, which GPU monitoring metric is most critical? A. Power Usage B. PCIe Bandwidth Utilization C. GPU Memory Usage D. GPU Core Utilization

Answer: C

When designing a data center specifically for AI workloads, which of the following factors is MOST critical to optimize for training large-scale neural networks? A. Maximizing the number of storage arrays to handle data volumes. B. Ensuring the data center has a robust virtualization platform. C. Deploying the maximum number of CPU cores available in each node. D. High-speed, low-latency networking between compute nodes.

Answer: D

Which networking feature is MOST important for supporting distributed training of large AI models across multiple data centers? A. Deployment of wireless networking to enable flexible node placement. B. Segregated network segments to prevent data leakage between AI tasks. C. Implementation of Quality of Service (QoS) policies to prioritize AI training traffic. D. High throughput with low latency WAN links between data centers.

Answer: D

You are deploying an AI model on a cloud-based infrastructure using NVIDIA GPUs. During the deployment, you notice that the model's inference times vary significantly across different instances, despite using the same instance type. What is the most likely cause of this inconsistency? A. Differences in the versions of the CUDA toolkit installed on the instances B. The model architecture is not suitable for GPU acceleration C. Network latency between cloud regions D. Variability in the GPU load due to other tenants on the same physical hardware

Answer: D

Your AI data center is running multiple high-power NVIDIA GPUs, and you've noticed an increase in operational costs related to power consumption and cooling. Which of the following strategies would be most effective in optimizing power and cooling efficiency without compromising GPU performance? A. Reduce GPU Utilization by Lowering Workload Intensity B. Increase the Cooling Fan Speeds of All Servers C. Switch to Air-Cooled GPUs Instead of Liquid-Cooled GPUs D. Implement AI-based Dynamic Thermal Management Systems

Answer: D