Q35 — AWS SAP-C02 Ch.2

Question 35 of 75 | ← Chapter 2

Q185. A company manufactures smart vehicles. The company uses a custom application to collect vehicle data. The vehicles use the MQTT protocol to connect to the application. The company processes the data in 5- minute intervals. The company then copies vehicle telematics data to on-premises storage. Custom applications analyze this data to detect anomalies. The number of vehicles that send data grows constantly. Newer vehicles generate high volumes of data. The on-premises storage solution is not able to scale for peak traffic, which results in data loss. The company must modernize the solution and migrate the solution to AWS to resolve the scaling challenges. Which solution will meet these requirements with the LEAST operational overhead?

Correct Answer: B. Use AWS loT Core to receive the vehicle data. Configure rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3. Create an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies

Explanation

B is the solution that will meet the requirements with the LEAST operational overhead. Option A involves using AWS IoT Greengrass to send vehicle data to Amazon MSK and storing it in S3. This option requires creating a custom Apache Kafka application to store data, which may require significant development effort and operational overhead. While using SageMaker to detect anomalies is a valid option, it may not be the most cost-effective or scalable solution. Option C involves using AWS IoT FleetWise to collect data and sending it to an Amazon Kinesis data stream, storing it in S3, and using AWS Glue for anomaly detection. This option adds complexity by requiring multiple services to work together, which could increase operational overhead. Option D involves using Amazon MQ for RabbitMQ to collect vehicle data and sending it to Amazon Kinesis Data Firehose delivery stream for storage in S3, and using Lookout for Metrics for anomaly detection. This option adds additional complexity by requiring the use of Amazon MQ, which could lead to increased operational overhead. Option B involves using AWS IoT Core to receive vehicle data and routing it to an Amazon Kinesis Data Firehose delivery stream for storage in S3. Additionally, Kinesis Data Analytics can be used to read the data from the stream and detect anomalies. This option minimizes operational overhead by using only a few services and enables easy scaling as the number of vehicles grows. Additionally, Kinesis Data Analytics provides built-in machine learning capabilities to detect anomalies, reducing the need for custom development. Overall, this option provides a simple, reliable, and cost-effective solution for modernizing the existing solution and migrating it to AWS while minimizing operational overhead.