You are building a machine learning model to use your web server logs to predict which users are most likely to buy a given product. Using your company’s unstructured web server log data stored in S3, you want to get your data into CSV format and load it into another S3 bucket so that you can use it for your machine learning algorithm.
Which of the following architectures will be the most efficient way to achieve this?

Answer options:

A.Load the log data into a Redshift cluster; use the UNLOAD Redshift command with a select statement to unload the data in CSV format to S3; SageMaker model uses the data to produce product purchase predictions.
B.Use a built-in classifier in an AWS Glue crawler that crawls the web server logs and outputs the log data to CSV format on your ML S3 bucket; SageMaker model uses the data to produce product purchase predictions.
C.Use AWS Schema Conversion tool to convert your web log data to CSV format and output it to your ML S3 bucket; run your SageMaker model on the new data to produce product purchase predictions.
D.Use AWS Snowball Edge and its lambda function capability to convert and then move the web log to S3 in CSV format; run your SageMaker model on the new data to produce product purchase predictions.

Answer: B
Option A is incorrect. Using Redshift as an intermediary step in this architecture is an expensive, in terms of implementation effort, an extraneous design decision that makes this option less efficient than Option
B.Option B is correct. AWS Glue has built-in classifiers designed specifically for web server log crawling. The crawler will generate CSV formatted data and output it to your ML S3 bucket. This option is the simplest to implement, and therefore the most efficient.
Option C is incorrect. The AWS Schema Conversion tool is used to convert a database from one database engine to another database engine, such as from PostgreSQL to MySQL. The AWS Schema Conversion tool will not work with unstructured web log data.
Option D is incorrect. AWS Snowball Edge is used to move data into and out of AWS. It would not be the most efficient way to transform your web log data to CSV and store it in your ML S3 bucket.
Reference:
Please see the Amazon Redshift Database developer guide titled Unloading Data, and Amazon Machine Learning developer guide titled Creating an Amazon ML Datasource from Data in Amazon Redshift, the AWS Schema Conversion Tool user guide titled What is the AWS Schema Conversion Tool?, and the Cloud Data Migration Guide, specifically the section on AWS Snowball Edge, and the AWS Glue developer guide titled Adding Classifiers to a Crawler.