AWS Certified Solutions Architect – Professional — Question 1001

A flood monitoring agency has deployed more than 10,000 water-level monitoring Sensors. Sensors send continuous data updates, and each update is less than
1 MB in size. The agency has a fleet of on-premises application servers. These servers receive updates from the sensors, convert the raw data into a human readable format, and write the results to an on-premises relational database server. Data analysts then use simple SQL queries to monitor the data.
The agency wants to increase overall application availability and reduce the effort that is required to perform maintenance tasks. These maintenance tasks, which include updates and patches to the application servers, cause downtime. While an application server is down, data is lost from sensors because the remaining servers cannot handle the entire workload.
The agency wants a solution that optimizes operational overhead and costs. A solutions architect recommends the use of AWS IoT Core to collect the sensor data.
What else should the solutions architect recommend to meet these requirements?

Answer options

• A. Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to .csv format, and insert it into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.
• B. Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to Apache Parquet format, and save it to an Amazon S3 bucket. Instruct the data analysts to query the data by using Amazon Athena.
• C. Send the sensor data to an Amazon Kinesis Data Analytics application to convert the data to .csv format and store it in an Amazon S3 bucket. Import the data into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.
• D. Send the sensor data to an Amazon Kinesis Data Analytics application to convert the data to Apache Parquet format and store it in an Amazon S3 bucket. Instruct the data analysts to query the data by using Amazon Athena.

Correct answer: B

Explanation

Using Amazon Kinesis Data Firehose paired with AWS Lambda to transform stream data into Apache Parquet format and store it in Amazon S3 offers a highly scalable, serverless pipeline with minimal operational overhead. Querying the columnar Parquet data in S3 using Amazon Athena allows analysts to run standard SQL queries cost-effectively without provisioning a database like Amazon Aurora. Amazon Kinesis Data Analytics is designed for complex real-time stream processing and is unnecessary and more expensive for this straightforward ingestion and formatting use case.