Topic 1 Question 7

AWS Certified Solutions Architect - Associate

Topic 1 Question 7
A company has an application that ingests incoming messages. Dozens of other applications and microservices then quickly consume these messages. The number of messages varies drastically and sometimes increases suddenly to 100,000 each second. The company wants to decouple the solution and increase scalability. Which solution meets these requirements?
- Persist the messages to Amazon Kinesis Data Analytics. Configure the consumer applications to read and process the messages.
- Deploy the ingestion application on Amazon EC2 instances in an Auto Scaling group to scale the number of EC2 instances based on CPU metrics.
- Write the messages to Amazon Kinesis Data Streams with a single shard. Use an AWS Lambda function to preprocess messages and store them in Amazon DynamoDB. Configure the consumer applications to read from DynamoDB to process the messages.
- Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SOS) subscriptions. Configure the consumer applications to process the messages from the queues.
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- 正解だと思う選択肢: D
  D makes more sense to me.
  
  👍 32
  rein_chau2022/10/08
- D. SNS Fan Out Pattern https://docs.aws.amazon.com/sns/latest/dg/sns-common-scenarios.html (A is wrong Kinesis Analysis does not 'persist' by itself.)
  
  👍 14
  Bevemo2022/11/06
- 正解だと思う選択肢: D
  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
  👍 9
  PhucVuu2023/04/04
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