Exam NCA-AIIO Topic 2 Question 4 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 4
Topic #: 2

A large manufacturing company is implementing an AI-based predictive maintenance system to reduce downtime and increase the efficiency of its production lines. The AI system must analyze data from thousands of sensors in real-time to predict equipment failures before they occur. However, during initial testing, the system fails to process the incoming data quickly enough, leading to delayed predictions and occasional missed failures. What would be the most effective strategy to enhance the system's real-time processing capabilities?

A. Reduce the number of sensors to decrease the amount of data the AI system must process B. Use a more complex AI model to enhance prediction accuracy C. Implement edge computing to preprocess sensor data closer to the source before sending it to the central AI system D. Increase the frequency of sensor data collection to provide more detailed inputs for the AI model

Suggested Answer: C Vote an answer

Implementing edge computing to preprocess sensor data closer to the source is the most effective strategy to enhance real-time processing capabilities for a predictive maintenance system. Using NVIDIA Jetson devices at the edge, raw sensor data can be filtered, aggregated, or preprocessed (e.g., via DeepStream), reducing the volume sent to the central GPU cluster (e.g., DGX). This lowers latency and ensures timely predictions, as outlined in NVIDIA's "Edge AI Solutions" and "AI Infrastructure for Enterprise." Reducing sensors (A) risks missing critical data. A more complex model (B) increases processingdemands, worsening delays. Higher data frequency (D) exacerbates the bottleneck. Edge computing is NVIDIA's recommended solution for real-time IoT workloads.

by Hubery at Nov 02, 2025, 02:44 PM

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