AWS Certified Machine Learning – Specialty — Question 348
An energy company has wind turbines, weather stations, and solar panels that generate telemetry data. The company wants to perform predictive maintenance on these devices. The devices are in various locations and have unstable internet connectivity.
A team of data scientists is using the telemetry data to perform machine learning (ML) to conduct anomaly detection and predict maintenance before the devices start to deteriorate. The team needs a scalable, secure, high-velocity data ingestion mechanism. The team has decided to use Amazon S3 as the data storage location.
Which approach meets these requirements?
Answer options
- A. Ingest the data by using an HTTP API call to a web server that is hosted on Amazon EC2. Set up EC2 instances in an Auto Scaling configuration behind an Elastic Load Balancer to load the data into Amazon S3.
- B. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to use Amazon Kinesis Data Firehose to send data to an Amazon Kinesis data stream that is configured to write to an S3 bucket.
- C. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to direct all MQTT data to an Amazon Kinesis Data Firehose delivery stream that is configured to write to an S3 bucket.
- D. Ingest the data over Message Queuing Telemetry Transport (MQTT) to Amazon Kinesis data stream that is configured to write to an S3 bucket.
Correct answer: C
Explanation
AWS IoT Core is designed to securely ingest high-velocity data from remote devices using Message Queuing Telemetry Transport (MQTT), which is highly efficient for unstable connections. Option C is correct because AWS IoT Core rules can directly deliver these messages to an Amazon Kinesis Data Firehose stream, which automatically batches and writes the data to Amazon S3. Option B is incorrect because routing Kinesis Data Firehose into a Kinesis data stream is an invalid and unnecessary architecture, while Options A and D do not leverage the managed scalability and MQTT support of AWS IoT Core.