Question 17
Domain 3You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
Correct answer: D
Explanation
Convolutional Neural Networks (CNNs) are designed for image data and use local filters to detect patterns like edges, textures, and defect shapes. Their convolution layers reuse weights across the image, which lowers computation while efficiently extracting visual features from product photos.
Why each option is right or wrong
A. Reinforcement learning
B. Recommender system
C. Recurrent Neural Networks (RNN)
D. Convolutional Neural Networks (CNN)
CNNs are the standard architecture for image classification tasks because convolution layers apply small shared kernels across the image, so the same weights are reused instead of learning separate parameters for every pixel. That weight sharing and local receptive fields reduce computation substantially while still capturing spatial features such as edges, textures, and irregular surface patterns that indicate defects in end-of-line product photos.