A company operates wind turbines, weather stations, and solar panels that produce telemetry. Devices are distributed across locations and have intermittent internet. Data scientists need a scalable, secure, high-throughput ingestion pipeline that stores raw telemetry in Amazon S3 for anomaly detection and predictive maintenance. Which ingestion approach satisfies these requirements?

The correct approach leverages AWS IoT Core for device connectivity and message routing, and Kinesis Data Firehose for scalable, direct ingestion to S3. AWS IoT Core is ideal for devices with intermittent connectivity, supporting MQTT and providing secure, bi-directional communication. Its rules engine allows direct routing of messages to various AWS services, including Kinesis Data Firehose. Kinesis Data Firehose is a fully managed service for delivering streaming data to S3, offering automatic scaling, buffering, and data transformation, which satisfies the high-throughput and scalable storage requirements. The incorrect options have limitations: Using EC2 web servers adds operational overhead, is less optimized for IoT telemetry, and might not scale as efficiently as managed services for high-throughput streaming data. Routing to Kinesis Data Firehose, then to a Kinesis data stream, and then to S3 introduces an unnecessary intermediate step (Kinesis data stream) when Firehose can directly deliver to S3. Sending telemetry directly from devices to a Kinesis data stream over MQTT is not natively supported; AWS IoT Core acts as the MQTT broker and integration point.