AWS Certified Machine Learning – Specialty — Question 246

A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.

Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

Answer options

• A. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
• B. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
• C. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
• D. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

Correct answer: B

Explanation

Option B is correct because a deep convolutional neural network (CNN) is well-suited for image classification tasks, and the softmax output layer is ideal for producing a probability distribution over the classes. Option A's linear output layer is less appropriate for multi-class classification. Options C and D employ a multilayer perceptron, which is generally less effective for image data compared to CNNs.