OK, for B I did some research, https://docs.aws.amazon.com/glue/latest/dg/add-job-streaming.html "You can create streaming extract, transform, and load (ETL) jobs that run continuously, consume data from streaming sources like Amazon Kinesis Data Streams, Apache Kafka, and Amazon Managed Streaming for Apache Kafka (Amazon MSK). The jobs cleanse and transform the data, and then load the results into Amazon S3 data lakes or JDBC data stores."

But how can you transform data using kinesis data analytics ??

Just because C is not going to work a DE use RDS so totally illogical A & B seem to have redundant streaming, transformation and query steps so not sure if these are the right choices but CDE are completely wrong anyway!

For A didn't know that Kinesis Analytics can transform the data as well: Amazon Kinesis Data Analytics provides built-in functions to filter, aggregate, and transform streaming data for advanced analytics. It processes streaming data with sub-second latencies, enabling you to analyze and respond to incoming data and streaming events in real time.

"Use SQL to query the transformed data" [which is in S3] requires Athena, thus D and E are out. DMS is nonsense here thus C is out.

why E is not right choise?

options A and B will meet these requirements.

A and B are correct. A uses Kinesis Data Streams for streaming, Kinesis Data Analytics for transformation, Kinesis Data Firehose for writing to S3, and Athena for SQL queries on S3 data. B uses Amazon MSK for streaming, AWS Glue for transformation and writing to S3, and Athena for SQL queries on S3 data.

Why E is incorrect?

To transform real-time streaming data from multiple sources, write it to Amazon S3, and query the transformed data using SQL, the company can use the following solutions: Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, and Amazon Kinesis Data Firehose. The transformed data can be queried using Amazon Athena. Therefore, options A and E are the correct answers. Option A is correct because it uses Amazon Kinesis Data Streams to stream data from multiple sources, Amazon Kinesis Data Analytics to transform the data, and Amazon Kinesis Data Firehose to write the data to Amazon S3. Amazon Athena can be used to query the transformed data in Amazon S3. Option E is also correct because it uses Amazon Kinesis Data Streams to stream data from multiple sources, AWS Glue to transform the data, and Amazon Kinesis Data Firehose to write the data to Amazon S3. Amazon Athena can be used to query the transformed data in Amazon S3.

DMS can move data from DBs to streaming services and cannot natively handle streaming data. Hence A.B makes sense. Also AWS Glue/ETL can handle MSK streaming https://docs.aws.amazon.com/glue/latest/dg/add-job-streaming.html.

The solutions that meet the requirements of streaming real-time data, transforming the data before writing to S3, and querying the transformed data using SQL are A and B. Option C: This option is not ideal for streaming real-time data as AWS DMS is not optimized for real-time data ingestion. Option D & E: These option are not recommended as the Amazon RDS query editor is not designed for querying data in S3, and it is not efficient for running complex queries.

The correct answers are options A & B

may Amazon RDS query editor to query the transformed data from Amazon S3 ? i don't think so, plz get link docs to that

Why not A & D?

A and B

Answer is : A & B