You are very correct. see supporting link https://jayendrapatil.com/aws-kinesis-data-streams-vs-kinesis-firehose/#:~:text=vs%20Kine...-,Purpose,into%20AWS%20products%20for%20processing.

Streaming ingestion provides low-latency, high-speed ingestion of stream data from Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka into an Amazon Redshift provisioned or Amazon Redshift Serverless materialized view.[1] Option B mentions Kinesis Data Firehose (now just Firehose), so this won't work. Option A is the correct answer. [1]https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html

Says who? They want to "query ingested data in near-real time", it does not say anything about historical data.

Redshift is a Data Warehouse in the first place, but the question says nothing about storing the data. They want to analyze it in near-real time, nobody says they need to store or access or analyze historical data.

The other options would not fully meet the requirements: B) Kinesis Firehose + Redshift would introduce latency since data must be loaded from Firehose into Redshift before querying. Redshift would lack real-time capabilities. C) An EC2 instance store and Kinesis Firehose to S3 with Athena querying would risk data loss from instance store if an instance reboots. Athena querying data in S3 also lacks real-time capabilities. D) Using EBS storage, Kinesis Firehose to Redis and subscribing to Redis may provide near-real-time ingestion and querying but risks data loss if an EBS volume or EC2 instance fails. Recovery requires re-hydrating data from a backup which impacts real-time needs.