AWS Certified Solutions Architect – Associate (SAA-C02) — Question 587

A space shuttle company that hosts its services on AWS must log performance data for each launch attempt. Each launch produces millions of small pieces of data every second. The total amount of data is terabytes in size. When the data is produced, the company must process the data and make the data available for
SQL queries.
What should a solutions architect do to meet these requirements?

Answer options

• A. Publish the data to an Amazon Kinesis data stream. Create an AWS Lambda function to process each record. Place the data in an Amazon S3 bucket. Use Amazon Athena to query the data.
• B. Publish the data to an Amazon Kinesis data stream. Create an AWS Lambda function to process each record. Place the data in an Amazon Redshift cluster. Use Amazon Redshift to query the data.
• C. Publish the data to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to process each message. Place the data in an Amazon Aurora database. Use Aurora to query the data.
• D. Publish the data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to process each message. Place the data in an Amazon S3 bucket. Use Amazon Athena to query the data.

Correct answer: A

Explanation

Amazon Kinesis data streams are designed to ingest high-throughput, real-time data streams containing millions of events per second. Storing the processed results in Amazon S3 and querying them via Amazon Athena offers a highly scalable, serverless, and cost-effective way to run SQL queries on terabytes of data. Other options, such as SQS FIFO queues, have strict throughput limits that cannot handle millions of messages per second, while loading streaming data directly into Aurora or Redshift at this scale is not architecturally optimal.