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Latest NCA-AIIO Exam Practice Questions

The practice questions for NCA-AIIO exam was last updated on 2025-06-03 .

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Question#1

Which component of the AI software ecosystem is responsible for managing the distribution of deep learning model training across multiple GPUs?

A. TensorFlow

B. CUDA

C. NCCL

D. cuDNN

Question#2

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 V

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.

Question#3

Which NVIDIA solution is specifically designed to accelerate the development and deployment of AI in healthcare, particularly in medical imaging and genomics?

A. NVIDIA TensorRT

B. NVIDIA Metropolis

C. NVIDIA Jetson

D. NVIDIA Clara

Question#4

Your AI team is deploying a real-time video processing application that leverages deep learning models across a distributed system with multiple GPUs. However, the application faces frequent latency spikes and inconsistent frame processing times, especially when scaling across different nodes. Upon review, you find that the network bandwidth between nodes is becoming a bottleneck, leading to these performance issues.
Which strategy would most effectively reduce latency and stabilize frame processing times in this distributed AI application?

A. Increase the number of GPUs per node.

B. Reduce the video resolution to lower the data load.

C. Optimize the deep learning models for lower complexity.

D. Implement data compression techniques for inter-node communication.

Question#5

Your team is developing a predictive maintenance system for a fleet of industrial machines. The system needs to analyze sensor data from thousands of machines in real-time to predict potential failures. You have access to a high-performance AI infrastructure with NVIDIA GPUs and need to implement an approach that can handle large volumes of time-series data efficiently.
Which technique would be most appropriate for extracting insights and predicting machine failures using the available GPU resources?

A. Applying a GPU-accelerated Long Short-Term Memory (LSTM) network to the time-series data.

B. Implementing a GPU-accelerated support vector machine (SVM) for classification.

C. Using a simple linear regression model on a sample of the data.

D. Visualizing the time-series data using basic line graphs to manually identify trends.

Exam Code: NCA-AIIOQ & A: 300 Q&AsUpdated: 2025-06-03

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