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Answer: Convert the API Gateway Regional endpoint to an edge-optimized endpoint and enable caching in the production stage.
The correct answer is A. By converting the API Gateway Regional endpoint to an edge-optimized endpoint and enabling caching in the production stage, the company can reduce the load on their database and Lambda functions. Edge-optimized endpoints cache responses at edge locations closer to users, which helps in alleviating the number of requests reaching the backend services. This approach minimizes the additional costs while effectively supporting the increased usage. While options B and C could also address the issue, they introduce additional complexity and higher costs, respectively. Option D, on the other hand, would limit the incoming calls but might negatively impact user experience by throttling legitimate traffic.
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A car rental company has developed a serverless REST API using Amazon API Gateway, AWS Lambda functions, and an Amazon Aurora MySQL Serverless DB cluster to serve its mobile app. Recently, the API was opened to partner mobile apps, leading to a significant increase in request volume and sporadic database memory errors. Traffic analysis shows that clients frequently make multiple identical HTTP GET requests within short intervals, with peak usage during business hours and around holidays. The company seeks a strategy to enhance support for this increased usage while minimizing additional costs. Which approach should the company adopt?
A
Convert the API Gateway Regional endpoint to an edge-optimized endpoint and enable caching in the production stage.
B
Implement an Amazon ElastiCache for Redis cache to store database call results and modify the Lambda functions to utilize this cache.
C
Adjust the Aurora Serverless DB cluster configuration to increase the maximum amount of available memory.
D
Enable throttling in the API Gateway production stage and set rate and burst values to control incoming call volume.
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