Question 173:
You work as a machine learning specialist for the airline traffic control agency of the federal government. Your machine learning team is responsible for producing the models that process all air traffic in-flight data to produce recommended flight paths for the aircraft currently aloft. The flight paths need to consider all of the prevailing conditions (weather, other flights in the path, etc.) that may affect an aircraft`s flight path. The data that your models need to process is massive in scale and requires large-scale data processing. How should you build the data transformation and feature engineering processing jobs so that you can process all of the flight data in real-time?
Answer options:
A.Run Glue ETL distributed data processing jobs to perform the transformation and feature engineering on the flight data in real-time and save the data to S3 for your model training. B.Use Kinesis Data Firehose to perform the transformation and feature engineering on the flight data in real-time and save the data to S3 for your model training. C.Run Apache Spark Streaming data processing jobs to perform the transformation and feature engineering on the flight data in real-time and save the data to S3 for your model training. D.Use a Kinesis Data Analytics SQL application to perform the transformation and feature engineering on the flight data in real-time and save the data to S3 for your model training.