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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions:

1. You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency. Which of the following networking technologies is most suitable for this scenario?

A) InfiniBand
B) Ethernet (1 Gbps)
C) Wi-Fi 6
D) Fiber Channel

2. You are tasked with creating a real-time dashboard for monitoring the performance of a large-scale AI system processing social media data. The dashboard should provide insights into trends, anomalies, and performance metrics using NVIDIA GPUs for data processing and visualization. Which tool or technique would most effectively leverage the GPU resources to visualize real-time insights from this high-volume social media data?

A) Relying solely on a relational database to handle the data and generate visualizations.
B) Employing a GPU-accelerated time-series database for real-time data ingestion and visualization.
C) Using a standard CPU-based ETL (Extract, Transform, Load) process to prepare the data for visualization.
D) Implementing a GPU-accelerated deep learning model to generate insights and feeding results into a CPU-based visualization tool.

3. You are responsible for managing an AI infrastructure where multiple data scientists are simultaneously running large-scale training jobs on a shared GPU cluster. One data scientist reports that their training job is running much slower than expected, despite being allocated sufficient GPU resources. Upon investigation, you notice that the storage I/O on the system is consistently high. What is the most likely cause of the slow performance in the data scientist's training job?

A) Inefficient data loading from storage
B) Overcommitted CPU resources
C) Incorrect CUDA version installed
D) Insufficient GPU memory allocation

4. 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) Increase the cooling fan speeds of all servers.
B) Implement AI-based dynamic thermal management systems.
C) Reduce GPU utilization by lowering workload intensity.
D) Switch to air-cooled GPUs instead of liquid-cooled GPUs.

5. A financial institution is implementing an AI-driven fraud detection system that needs to process millions of transactions daily in real-time. The system must rapidly identify suspicious activity and trigger alerts, while also continuously learning from new data to improve accuracy. Which architecture is most appropriate for this scenario?

A) Single GPU server with local SSD storage for both training and inference
B) CPU-based servers with cloud storage for centralized processing
C) Hybrid setup with multi-GPU servers for training and edge devices for inference
D) Edge-only deployment with ARM processors for both training and inference

Solutions: