D makes more sense to me.

Keywords: - The number of messages varies drastically - Sometimes increases suddenly to 100,000 each second A: Incorrect - Don't confuse between Kinesis Data Analytics and Kinesis Data Stream =)) Kinesis Data Analytics will get the data from Kinesis Data Stream or Kinesis Data FireHose or MSK (Managed Stream for apache Kafka) for analytic purpose. It can not consume message and send to applications. B: Incorrect - Base on the keywords -> Auto Scaling group not scale well because it need time to check the CPU metric and need time to start up the EC2 and the messages varies drastically. Example: we have to scale from 10 to 100 EC2. Our servers may be down a while when it was scaling. C: Incorrect - Kinesis Data Streams can handle this case but we should increase the more shards but not single shard. D: Correct: We can handle high workload well with fan-out pattern SNS + multiple SQS -> This is good for use case: - The number of messages varies drastically - Sometimes increases suddenly to 100,000 each second

By default, an SQS queue can handle a maximum of 3,000 messages per second. However, you can request higher throughput by contacting AWS Support. AWS can increase the message throughput for your queue beyond the default limits in increments of 300 messages per second, up to a maximum of 10,000 messages per second. It's important to note that the maximum number of messages per second that a queue can handle is not the same as the maximum number of requests per second that the SQS API can handle. The SQS API is designed to handle a high volume of requests per second, so it can be used to send messages to your queue at a rate that exceeds the maximum message throughput of the queue.

D. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SQS) subscriptions. Configure the consumer applications to process the messages from the queues. This solution uses Amazon SNS and SQS to publish and subscribe to messages respectively, which decouples the system and enables scalability by allowing multiple consumer applications to process the messages in parallel. Additionally, using Amazon SQS with multiple subscriptions can provide increased resiliency by allowing multiple copies of the same message to be processed in parallel.

oh... I confused between Kinesis Data Analytics and Kinesis Data Stream as you mentioned... I solved several this type of questions, but SNS is always about 'notification', so i choose A. but i think Kinesis Data Analytics is just wrong, so D is most correct answer.

of course, the answer is D

That limit is for SQS FIFO queue not for standard queue.

Selected Answer: D I choose D because the question says "decouple/scalable" and the best services for that are SNS/SQS.

D the best choose

The other options are not correct. Like Option A: Kinesis Data Analytics is designed for real-time data processing and analytics. Not for message queuing and decoupling.

D makes sense

D is correct answer

Ans is D, as AWS SQS is virtually limitlessly scalable, we can increase the consumer count to handle scalability.

Letra D!

to use fan out with sns and sqs

vote d

Use SNS + multiple SQS queues to achieve a highly scalable, decoupled, and reliable architecture for sudden and massive message ingestion and processing.

Fan out modal SNS with multiple SQS for decoupling so answer is D

using an SQS queues to decouple this soluton will be the best approach since an SQS queue is very scalable and the number of messages to scale can be incremented by the user manually or by the use of security groups

Amazon SNS (Simple Notification Service) can deliver messages to SQS (Simple Queue Service) by subscribing an SQS queue to an SNS topic. This is a common pattern for decoupling systems and ensuring reliable message delivery. Amazon SNS can handle very high throughput 100K.

D is correct

my self

d is correct answer so you have to approve me answer as soon as possible