AWS Certified Big Data – Specialty — Question 15

A company that manufactures and sells smart air conditioning units also offers add-on services so that customers can see real-time dashboards in a mobile application or a web browser. Each unit sends its sensor information in JSON format every two seconds for processing and analysis. The company also needs to consume this data to predict possible equipment problems before they occur. A few thousand pre-purchased units will be delivered in the next couple of months. The company expects high market growth in the next year and needs to handle a massive amount of data and scale without interruption.
Which ingestion solution should the company use?

Answer options

• A. Write sensor data records to Amazon Kinesis Streams. Process the data using KCL applications for the end-consumer dashboard and anomaly detection workflows.
• B. Batch sensor data to Amazon Simple Storage Service (S3) every 15 minutes. Flow the data downstream to the end-consumer dashboard and to the anomaly detection application.
• C. Write sensor data records to Amazon Kinesis Firehose with Amazon Simple Storage Service (S3) as the destination. Consume the data with a KCL application for the end-consumer dashboard and anomaly detection.
• D. Write sensor data records to Amazon Relational Database Service (RDS). Build both the end-consumer dashboard and anomaly detection application on top of Amazon RDS.

Correct answer: C

Explanation

The correct answer is C because Amazon Kinesis Firehose can efficiently handle real-time streaming data and deliver it to Amazon S3, making it suitable for both the end-consumer dashboard and anomaly detection. Option A, while using Kinesis Streams, does not directly mention Firehose, which is optimized for this use case. Option B suggests batching data every 15 minutes, which may not provide the real-time insights needed. Option D relies on Amazon RDS, which may not scale as effectively for high-velocity data ingestion compared to Kinesis solutions.