You work for a startup e-commerce site that sells various consumer products. Your company has just launched its e-commerce website. The site provides the capability for your users to rate their purchases and the products they have purchased from your e-commerce site. You would like to use the review data to build a recommender machine learning model.
Since your e-commerce site is very new, you don’t yet have a very large review dataset to use for your recommendation model. You have decided to use the Amazon Customer Reviews dataset from the AWS website as a first data source for your machine learning model. Since your website sells similar products to the products sold on Amazon, you will use the Amazon Customer Reviews dataset as the basis for your initial training runs of your model. Once you have enough data from your own e-commerce site, you’ll use that data.
Your goal is to perform sentiment analysis on the review dataset to create your own dataset that will be the source used for your recommender machine learning model. Which set of AWS services would you use to build your data pipeline to produce your sentiment dataset for use by your SageMaker model?

Answer options:

A.S3 -> AWS Glue ETL -> Comprehend -> S3 -> SageMaker
B.S3 -> AWS Glue ETL -> Comprehend -> S3 -> Athena -> QuickSite -> SageMaker
C.S3 -> Kinesis Data Firehose -> Comprehend -> S3 -> SageMaker
D.S3 -> Kinesis Data Firehose -> Lambda -> S3 -> SageMaker

Answer: A
Option A is correct. The Amazon Customer Reviews dataset is stored on S3. You can use an AWS Glue ETL job to read the reviews from the Amazon dataset. The ETL job calls Comprehend for each review to get the sentiment for that review. The ETL job stores the sentiment enriched review data onto another S3 bucket in your account. Your SageMaker model uses the S3 bucket in your account as its dataset source for training your recommender model.
Option B is incorrect. This option has unnecessary steps. Specifically, you don’t need Athena and QuickSite to produce your sentiment enriched dataset for your machine learning model.
Option C is incorrect. The option uses Kinesis Data Firehose unnecessarily. The Amazon Customer Reviews dataset is stored on S3. There is no need to stream the data when you can simply read it using an ETL job. If you used Kinesis Data Firehose to stream the data, you would have to write a lambda function to call Comprehend for each streamed review data row.
Option D is incorrect. The option uses Kinesis Data Firehose unnecessarily. The Amazon Customer Reviews dataset is stored on S3. There is no need to stream the data when you can simply read it using an ETL job. That being said, this option does correctly combine Kinesis Data Firehose and lambda. However, it lacks the Comprehend service. You would have to write your own sentiment analysis in your lambda function.
Reference:
Please see the data repository titled Registry of Open Data on AWS, the AWS Machine Learning blog titled How to scale sentiment analysis using Amazon Comprehend, AWS Glue and Amazon Athena, and the data set titled Amazon Customer Reviews Dataset.
Here is a diagram of the proposed solution: