Question 158:
You work as a machine learning specialist at a retail clothing chain. Your team builds a model that uses Kinesis Data Firehose to ingest transaction records from the chain’s many (50,000) stores throughout the country. You are building your training data store using your streaming transactions received from Kinesis Data Firehose. The transaction records used for training require simple transformations, and you need to combine some attributes and drop other attributes. Also, you need to retrain the model daily. Which option will meet your requirements with the least effort?
Answer options:
A.Have the Kinesis Data Firehose stream your transaction records to Kinesis Data Analytics, where you transform the transaction records and combine/drop attributes using Apache Flink and store the transformed records to S3. B.Have the Kinesis Data Firehose stream your transaction records to S3. Launch an ECS cluster that runs the transformation logic as tasks that transform and combine/drop attributes on the data records on Amazon S3 C.Have the Kinesis Data Firehose stream your transaction records to S3. Run an EMR cluster with Apache Hadoop and Apache Presto performing the transformation logic. Run the cluster every day to transform and combine attributes of the records on S3. D.Use Kinesis Data Streams instead of Kinesis Data Firehose to stream the transaction records to S3.Then transform and combine attributes using a Glue ETL job and store the results on S3.