AWS Certified Data Analytics – Specialty — Question 41

A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage.
Which solution meets these requirements with the least operational overhead?

Answer options

• A. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.
• B. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
• C. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
• D. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

Correct answer: D

Explanation

Option D is the most efficient as it utilizes Amazon Kinesis Data Firehose, which automatically handles data ingestion and transformation, significantly reducing operational overhead. Options A and C rely on Kinesis Data Streams combined with AWS Lambda for PHI removal, which introduces more complexity and management. Option B, while using Kinesis Data Firehose, delays PHI removal until after data is stored in S3, which is less efficient and secure.