CompTIA DataX DY0-001 (V1) Practice Question

The correct answer is sensor fusion. Sensor fusion is the process of integrating data from multiple sensors to produce more accurate, reliable, and comprehensive information than what can be obtained from a single sensor alone. In this scenario, combining data from cameras (which struggle in low light), LiDAR (which can be affected by reflective surfaces), and thermal imaging allows the robot to build a robust perception of its environment that compensates for the weaknesses of each individual sensor.

• Data augmentation is a training-phase technique used to artificially increase the diversity of a dataset by applying transformations like rotation or brightness changes to existing images. It improves a model's generalization but does not involve combining data from different sensor types during operation.
• Semantic segmentation is the task of classifying each pixel in an image to a specific category (e.g., crop, weed, soil). This is a perception task that would be performed on the sensor data, and its accuracy would be enhanced by sensor fusion, but it is not the process of combining the sensor data itself.
• Object tracking involves following detected objects over time and across video frames. It is a downstream application that relies on the initial detection and perception provided by the sensor system; it is not the core method for integrating the various sensor inputs.