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A healthcare company develops a patient monitoring application that uses AWS AppSync and an AWS...

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A healthcare company develops a patient monitoring application that uses AWS AppSync and an AWS Lambda function. The application stores patient data in an Amazon DynamoDB database. The application processes real-time patient vital signs from thousands of IoT devices. The application must support queries from medical staff who access patient historical data. A developer notices significant latency when medical staff query patient histories, specifically for frequently-accessed records of patients currently under observation. The developer observes that the Lambda functions are consuming high read capacity units (RCUs) from DynamoDB. The developer needs to optimize the application ' s performance and maintain data consistency. Which solution will meet these requirements?

A.

Enable response caching in AppSync with a TTL value of 1 hour for all queries. Update the Lambda code to use in-memory caching.

B.

Increase the Lambda function memory allocation to maximum. Configure provisioned concurrency for the function.

C.

Create a caching Lambda function that aggregates patient data periodically and stores it in Amazon S3. Implement a read-through cache pattern.

D.

Implement in-memory caching by using Amazon ElastiCache for frequently-accessed patient records. Configure the Lambda functions to check the cache before querying the database. Set up cache invalidation when patient data is updated.

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