You work as a machine learning specialist for an online flight booking service that finds the lowest cost flights based on user input such as flight dates, origin, destination, number of layovers, and other factors. Your machine learning team gathers data from many sources, including airline flight databases, credit agencies, etc., to use in your model. You need to transform this data for your model training and in real-time for your model inference requests. What is the most efficient way to build these transformations into your model workflow?

Answer options:

A.Use Apache Spark ML Streaming to deploy an inference pipeline that reuses the data transforms you developed for your training model.
B.Use Apache Spark ML Serving to deploy an inference pipeline that reuses the data transforms you developed for your training model.
C.Use Apache Spark MLlib to deploy an inference pipeline that reuses the data transforms you developed for your training model.
D.Use a SageMaker lifecycle configuration to automate the reuse of the data transforms you developed for your training model.

Correct Answer: B
Option A is incorrect. Apache Spark Streaming supports real-time processing of streaming data. Apache Spark Streaming cannot be used to reuse the data transforms you developed for your training model in your inference requests.
Option B is correct. You can use SageMaker Spark ML Serving containers that reuse the data transformers developed for training models in your inference requests.
Option C is incorrect. Apache Spark MLlib is a machine learning library that runs algorithms designed to scale across clusters for classification, regression, clustering, collaborative filtering. Apache Spark MLlib cannot be used to reuse the data transforms you developed for your training model in your inference requests.
Option D is incorrect. SageMaker lifecycle configurations are used to install packages or sample notebooks on your notebook instances, configure notebook instance networking and security, or use a shell script to customize notebook instances. SageMaker lifecycle configurations cannot be used to reuse the data transforms you developed for your training model in your inference requests.
References:
Please see the Amazon Machine Learning Lens AWS Well-Architected Framework titled Feature Engineering (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/feature-engineering.html),
The Amazon SageMaker developer guide titled Deploy an Inference Pipeline (https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipelines.html),
The Amazon SageMaker developer guide titled Use SparkML Serving with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/sparkml-serving.html),
The Amazon SageMaker developer guide titled Customize a Notebook Instance Using a Lifecycle Configuration Script (https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html),
The Apache Spark page titled Streaming (https://spark.apache.org/streaming/),
The Apache Spark page titled MLlib (https://spark.apache.org/mllib/)