Discover how the Amazon SageMaker XGBoost algorithm delivers the most accurate predictions for automating quality control in manufacturing using sensor data and manual inspection results.
Table of Contents
Question
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
B. Amazon SageMaker XGBoost algorithm
C. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
D. A convolutional neural network (CNN) and ResNet
Answer
B. Amazon SageMaker XGBoost algorithm
Explanation
The Amazon SageMaker XGBoost algorithm is the most suitable choice for predicting product quality based on the sensor data and manual inspection results. XGBoost is a powerful gradient boosting framework that combines multiple weak learners to create a strong predictive model. It excels at handling structured data, such as the numerical sensor metrics and categorical inspection results in this scenario.
The problem described is a multi-class classification task, where the goal is to classify products into three categories: good quality, replacement market quality, or scrap quality. XGBoost is well-suited for this type of problem due to its ability to handle complex relationships between features and its robustness to outliers and missing data.
Other options:
A. Amazon SageMaker DeepAR is designed for time series forecasting, which is not directly applicable to this classification problem.
C. Latent Dirichlet Allocation (LDA) is an unsupervised learning algorithm used for topic modeling in text data, not for structured data classification.
D. Convolutional Neural Networks (CNNs) and ResNet are primarily used for image and video data, not structured sensor data.
In summary, the Amazon SageMaker XGBoost algorithm is the most appropriate choice for accurately predicting product quality based on sensor data and manual inspection results in this manufacturing scenario.
Amazon AWS Certified Machine Learning – Specialty (MLS-C01) certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Amazon AWS Certified Machine Learning – Specialty (MLS-C01) exam and earn Amazon AWS Certified Machine Learning – Specialty (MLS-C01) certification.