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Answer: Using two Lambda functions with Amazon SQS for integration to manage information reception and database loading.
Option D is correct because using Amazon SQS (Simple Queue Service) with AWS Lambda provides a scalable and decoupled way to handle the high volume of data. One Lambda function receives the information and places messages into an SQS queue. The second Lambda function processes these messages and loads the information into the Amazon Aurora PostgreSQL database. This approach minimizes the configuration effort and improves scalability as SQS automatically scales to handle the load and ensures that data is processed in an orderly fashion.
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In the design of an application utilizing AWS Lambda for data reception via Amazon API Gateway and storage in an Amazon Aurora PostgreSQL database, a solutions architect is tasked with recommending a scalable design that minimizes configuration effort, especially during the proof-of-concept phase where high data volumes necessitate increased Lambda quotas. What is the recommended solution?
A
Refactoring to Apache Tomcat on Amazon EC2 instances with JDBC for database connectivity.
B
Switching to Amazon DynamoDB with a DAX cluster for accelerated API calls.
C
Implementing two Lambda functions with Amazon SNS for integration to handle information reception and database loading.
D
Using two Lambda functions with Amazon SQS for integration to manage information reception and database loading.
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