Question 112:
You work for a healthcare data provider company that gathers real-time streaming data from healthcare plan participants who have agreed to allow their insurance company to use their health data gathered by their wearable technology, such as internet-connected watches and step counters. The plan participants receive discounts on their healthcare plan fees when participating in the data streaming effort. You are on the machine learning team that will use this data to better predict healthcare issues based on the gathered wearable data. Due to the secure nature of this personal information, you need to build encryption into your data pipeline for this effort. How would you construct your data pipeline in the most secure way to ensure your data is encrypted as it moves from the IoT wearable devices to your machine learning data source?
Answer options:
A.Use IoT Analytics to gather the streaming data from the IoT devices, encrypt the data, and send it to your machine learning data source. B.Use Kinesis Data Streams to gather the streaming data from the IoT devices. Have Kinesis Data Streams be the source of a Kinesis Data Firehose delivery stream which encrypts your data using an AWS Key Management Service (AWS KMS) key before storing the data at rest and then delivers the data to your S3 bucket used for your machine learning models. C.Use Kinesis Data Streams to gather the streaming data from the IoT devices and encrypt your data using an AWS Key Management Service (AWS KMS) key before storing the data at rest. Then have Kinesis Data Streams be the source of a Kinesis Data Firehose delivery stream which delivers the data to your S3 bucket used for your machine learning models. D.Use Kinesis Data Analytics to gather the streaming data from the IoT devices, encrypt the data, and send it to your machine learning data source.