You work as a machine learning specialist for a large software company that has several huge data centers around the world. Your company has realized they could do a better job managing their data center cooling by implementing a machine learning system to automate the management of the many controls used to control their data center power usage. The machine learning model needs to input the unlabeled data from building management systems such as chillers, pumps, cooling units, the actual load from systems usage, etc. You want to run your model to process real-time inferences while also learning from the new inferences.
Which combination of SageMaker algorithms and learning techniques should you use for your model to predict settings that optimize cooling on an ongoing basis?

Answer options:

A.Supervised learning using a regression algorithm
B.Unsupervised learning using a Multilayer Perceptron algorithm
C.Reinforcement learning
D.Unsupervised learning using a Sequence-to-Sequence Neural Network algorithm
E.Supervised learning using a Feedforward Neural Network algorithm

Answer: C
Option A is incorrect. A regression algorithm is not the best choice for optimizing unlabeled data; regression algorithms require labeled data.
Option B is incorrect. The Multilayer Perceptron algorithm is used for speech recognition and translation.
 Option C is correct. Reinforcement learning is used to update your model as new inference observations are encountered continually.
Option D is incorrect. The Sequence-to-Sequence Neural Network algorithm is used for machine translation and question answering systems. 
Option E is incorrect. The Feedforward Neural Network algorithm is a simple neural network that cannot handle a complex problem like data center power usage effectiveness management. 
Reference:
Please see the article titled Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning, the Wikipedia article titled Reinforcement learning.