Question 163:
You are a machine learning specialist working for an online retail shopping site. Your machine learning team is responsible for building out a machine learning environment using SageMaker Studio to make possible the running of models used to predict online sales and product pipeline optimization. Your team also needs to optimize the data ingestion solution into your data lake that is the primary source for your machine learning models. Your ingestion solution will also facilitate analytics (real-time and interactive analytics of historical data), clickstream analysis, as well as product recommendations. Which option best meets your team’s requirements?
Answer options:
A.Use Athena as the data catalog of your data lake files, use Kinesis Data Streams and Kinesis Data Analytics for historical data insights, use Glue for clickstream analytics, and create personalized product recommendations. B.Use Glue as the data catalog of your data lake files, use Kinesis Data Streams and Kinesis Data Analytics for historical data insights, use Kinesis Data Firehose to deliver your data to ElasticSearch for clickstream analytics, and leverage Kibana dashboards to create personalized product recommendations. C.Use Athena as the data catalog of your data lake files, use Kinesis Data Streams and Kinesis Data Analytics to generate near-real-time data insights, use Kinesis Data Firehose for clickstream analytics, and use Glue to create personalized product recommendations. D.Use Glue as the data catalog of your data lake files, use Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights, use Kinesis Data Firehose to deliver your data ElasticSearch for clickstream analytics, and use EMR to generate personalized product recommendations.