A manufacturing company is deploying a smart assembly line where automated robotic arms perform high-precision tasks. Each arm reports telemetry status messages (such as 'calibrating', 'started', and 'completed') that must be processed in the exact order they are generated for each individual arm to maintain a digital twin representation in a database. If messages are processed out of order, the digital twin state becomes corrupt. During peak production hours, the volume of telemetry messages spikes significantly. The downstream backend consists of a fleet of worker processes running in an Auto Scaling group. Which combination of steps should a solutions architect recommend to decouple the ingestion tier from the backend workers while ensuring strict ordering per assembly arm and preventing message loss? (Select TWO.)
- Create an Amazon SQS FIFO queue and publish the telemetry events to it, using the assembly arm ID as the Message Group ID.Cevap
- Configure the Auto Scaling group workers to poll the Amazon SQS FIFO queue and delete messages after they are successfully processed.Cevap
- CCreate a standard Amazon SQS queue and publish the telemetry events using the assembly arm ID as the Message Group ID.
- DCreate an Amazon SQS FIFO queue and publish the telemetry events using the assembly arm ID as the Message Deduplication ID.
- EConfigure the Auto Scaling group workers to retrieve messages from a standard Amazon SQS queue, utilizing a client-side sorting library to order messages chronologically.