AWS Certified Solutions Architect – Associate (SAA-C02) — Question 217

A company is using a fleet of Amazon EC2 instances to ingest data from on-premises data sources. The data is in JSON format and ingestion rates can be as high as 1 MB/s. When an EC2 instance is rebooted, the data in-flight is lost. The company's data science team wants to query ingested data in near-real time.
Which solution provides near-real-time data querying that is scalable with minimal data loss?

Answer options

• A. Publish data to Amazon Kinesis Data Streams. Use Kinesis Data Analytics to query the data.
• B. Publish data to Amazon Kinesis Data Firehose with Amazon Redshift as the destination. Use Amazon Redshift to query the data.
• C. Store ingested data in an EC2 instance store. Publish data to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the data.
• D. Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Publish data to Amazon ElastiCache for Redis. Subscribe to the Redis channel to query the data.

Correct answer: A

Explanation

Option A is correct because Amazon Kinesis Data Streams allows for real-time data ingestion and processing with minimal data loss. The other options either involve additional latency in querying (like Redshift and Athena) or do not provide a suitable mechanism for real-time data querying (like using ElastiCache).