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Question 281 - MLS-C01 discussion

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A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?

A.
Amazon SageMaker DeepAR forecasting algorithm
Answers
A.
Amazon SageMaker DeepAR forecasting algorithm
B.
Amazon SageMaker XGBoost algorithm
Answers
B.
Amazon SageMaker XGBoost algorithm
C.
Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
Answers
C.
Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
D.
A convolutional neural network (CNN) and ResNet
Answers
D.
A convolutional neural network (CNN) and ResNet
Suggested answer: D

Explanation:

A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1.ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.

References:

Convolutional Neural Networks (CNNs / ConvNets)

PyTorch ResNet: The Basics and a Quick Tutorial

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asked 16/09/2024
PATRICK KOUOBOU
29 questions
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