Question 15:
You work for a retail athletic footwear company. Your company has just completed the production of a new running shoe that contains IoT sensors in the shoe. These sensors are used to enhance the runner’s running experience by giving detailed data about foot plant, distance, acceleration, gait, and other data points for use in personal running performance analysis. You are on the machine learning team assigned the task of building a machine learning model to use the shoe IoT sensor data to make predictions of shoe life expectancy based on user wear and tear of the shoes. Instead of just using raw running miles as the predictor of shoe life, your model will use all of the IoT sensor data to produce a much more accurate prediction of the remaining life of the shoes. You are in the process of building your dataset for training your model and running inferences from your model. You need to clean the IoT sensor data before using it for training or use it to provide inferences from your inference endpoint. You have decided to use Spark ML jobs within AWS Glue to build your feature transformation code. Which machine learning packages/engines are the best choices for building your IoT sensor data transformer tasks in the simplest way possible? (Select THREE)
Answer options:
A.MLeap B.MLlib C.SparkML Serving Container D.SparkML Batch Transform E.MLTransform F.SparkML MapReduce