D. Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data. This solution will meet the requirements with the least operational overhead as it uses Amazon Kinesis Data Firehose, which is a fully managed service that can automatically handle the data collection, data transformation, encryption, and data storage in near-real time. Kinesis Data Firehose can automatically store the data in Amazon S3 in Apache Parquet format for further processing. Additionally, it allows you to create an Amazon Kinesis Data Analytics application to analyze the data in near real-time, with no need to manage any infrastructure or invoke any Lambda function. This way you can process a large amount of data with the least operational overhead.

A. requires invoking an Lambda to send the data to the analytics application. This introduces additional operational overhead and complexity. B. While EMR is a powerful tool for big data processing, it requires more operational management and configuration compared to Kinesis Data Analytics. C. introduces unnecessary complexity by involving EMR for data analysis when Kinesis Data Analytics can perform the analysis in a more streamlined and automated manner. Therefore, option D is the most suitable solution as it leverages Kinesis Data Firehose for data ingestion, stores the data in S3, and utilizes Kinesis Data Analytics for near-real-time analysis, providing a low operational overhead solution for data usage analysis and encryption.

A and B are out. Kinesis Data Streams cannot directly send data to S3 by itself

D. Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data. This solution will meet the requirements with the least operational overhead as it uses Amazon Kinesis Data Firehose, which is a fully managed service that can automatically handle the data collection, data transformation, encryption, and data storage in near-real time. Kinesis Data Firehose can automatically store the data in Amazon S3 in Apache Parquet format for further processing. Additionally, it allows you to create an Amazon Kinesis Data Analytics application to analyze the data in near real-time, with no need to manage any infrastructure or invoke any Lambda function. This way you can process a large amount of data with the least operational overhead.

Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data

D. Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data

D: https://www.examtopics.com/discussions/amazon/view/82022-exam-aws-certified-solutions-architect-associate-saa-c02/

D. Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Create an Amazon Kinesis Data Analytics application to analyze the data. Amazon Kinesis Data Firehose can automatically encrypt and store the data in Amazon S3 in Apache Parquet format for further processing, which reduces the operational overhead. It also allows for near-real-time data analysis using Kinesis Data Analytics, which is a fully managed service that makes it easy to analyze streaming data using SQL. This solution eliminates the need for setting up and maintaining an EMR cluster, which would require more operational overhead.