AWS Certified Machine Learning – Specialty — Question 72

A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute
(RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training.
Which is the MOST suitable predictive model that can be deployed into production?

Answer options

• A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
• B. This data requires an unsupervised learning algorithm. Use Amazon SageMaker k-means to cluster the data.
• C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
• D. This data is already formulated as a time series. Use Amazon SageMaker seq2seq to model the time series.

Correct answer: A

Explanation

The correct answer is A because it specifies the use of a recurrent neural network (RNN), which is well-suited for sequential data and can learn temporal dependencies necessary for predicting engine maintenance needs. Option B is incorrect as it suggests unsupervised learning, which does not leverage the time-dependent nature of engine faults. Option C is wrong since it proposes a convolutional neural network (CNN), which is not optimal for sequential data prediction like this. Option D assumes the data is a time series but does not utilize the appropriate model needed for supervised learning.