AWS Certified Solutions Architect – Professional (SAP-C02) — Question 513

Accompany is building an application to collect and transmit sensor data from a factory. The application will use AWS IoT Core to send data from hundreds of devices to an Amazon S3 data lake. The company must enrich the data before loading the data into Amazon S3.

The application will transmit the sensor data every 5 seconds. New sensor data must be available in Amazon S3 less than 30 minutes after the application collects the data. No other applications are processing the sensor data from AWS IoT Core.

Which solution will meet these requirements MOST cost-effectively?

Answer options

• A. Create a topic in AWS IoT Core to ingest the sensor data. Create an AWS Lambda function to enrich the data and to write the data to Amazon S3. Configure an AWS IoT rule action to invoke the Lambda function.
• B. Use AWS IoT Core Basic Ingest to ingest the sensor data. Configure an AWS IoT rule action to write the data to Amazon Kinesis Data Firehose. Set the Kinesis Data Firehose buffering interval to 900 seconds. Use Kinesis Data Firehose to invoke an AWS Lambda function to enrich the data, Configure Kinesis Data Firehose to deliver the data to Amazon S3.
• C. Create a topic in AWS IoT Core to ingest the sensor data. Configure an AWS IoT rule action to send the data to an Amazon Timestream table. Create an AWS Lambda, function to read the data from Timestream. Configure the Lambda function to enrich the data and to write the data to Amazon S3.
• D. Use AWS loT Core Basic Ingest to ingest the sensor data. Configure an AWS IoT rule action to write the data to Amazon Kinesis Data Streams. Create a consumer AWS Lambda function to process the data from Kinesis Data Streams and to enrich the data. Call the S3 PutObject API operation from the Lambda function to write the data to Amazon S3.

Correct answer: B

Explanation

Option B is the most cost-effective solution because AWS IoT Core Basic Ingest eliminates messaging costs by bypassing the message broker. Kinesis Data Firehose natively buffers the high-frequency sensor streams (such as setting a 900-second window, which easily satisfies the 30-minute latency limit) and integrates with AWS Lambda to transform/enrich the data in batches before saving it to S3, reducing S3 PUT request costs. Options A and D are much more expensive because they invoke Lambda functions and S3 APIs individually for each sensor reading rather than batching them efficiently.