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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions:

1. A transportation company wants to implement AI to improve the safety and efficiency of its autonomous vehicle fleet. They need a solution that can handle real-time data processing, deep learning model inference, and high-throughput workloads. Which NVIDIA solution should they consider deploying?

A) NVIDIA Clara
B) NVIDIA Jetson
C) NVIDIA Drive
D) NVIDIA DeepStream

2. You are tasked with deploying a real-time recommendation system for an e-commerce platform using NVIDIA AI infrastructure. The system needs to process millions of user interactions per second to provide personalized recommendations instantly. Which NVIDIA solution is best suited to handle this workload efficiently?

A) NVIDIA Triton Inference Server
B) NVIDIA Clara
C) NVIDIA TensorRT
D) NVIDIA DGX Station

3. When virtualizing an infrastructure that includes GPUs to support AI workloads, what is one critical factor to consider to ensure optimal performance?

A) Increase the number of virtual CPUs assigned to each VM
B) Disable hyper-threading on the host machine
C) Use GPU sharing technologies, like NVIDIA GRID, to allocate resources dynamically
D) Assign more storage to each virtual machine

4. You are assisting in a project where the senior engineer requires you to create visualizations of system resource usage during the training of an AI model. The training was conducted using multiple NVIDIA GPUs over several hours. The goal is to present the results in a way that highlights periods of high resource utilization and potential bottlenecks. Which type of visualization would best illustrate periods of high resource utilization and potential bottlenecks during the training process?

A) Box plot showing the distribution of resource usage.
B) Pie chart showing the proportion of time each GPU was utilized.
C) Stacked bar chart showing cumulative resource usage.
D) Heatmap showing GPU utilization over time.

5. An organization is deploying a large-scale AI model across multiple NVIDIA GPUs in a data center. The model training requires extensive GPU-to-GPU communication to exchange gradients. Which of the following networking technologies is most appropriate for minimizing communication latency and maximizing bandwidth between GPUs?

A) Fibre Channel
B) InfiniBand
C) Ethernet
D) Wi-Fi

Solutions: