DVA-C02 · Question #610
A developer is building an application to process a stream of customer orders. The application sends processed orders to an Amazon Aurora MySQL database. The application needs to process the orders in
The correct answer is A. Use Amazon Kinesis Data Streams to stream the orders. Use an AWS Lambda function to. Amazon Kinesis Data Streams supports ordered and batched data processing. By using a Lambda function with event source mapping and setting MaximumBatchingWindowInSeconds, the developer can batch records over a time window (up to 300 seconds) before sending them to the Aurora MySQ
Question
A developer is building an application to process a stream of customer orders. The application sends processed orders to an Amazon Aurora MySQL database. The application needs to process the orders in batches. The developer needs to configure a workflow that ensures each record is processed before the application sends each order to the database. Which solution meets these requirements?
Options
- AUse Amazon Kinesis Data Streams to stream the orders. Use an AWS Lambda function to
- BUse Amazon SQS to stream the orders. Use an AWS Lambda function to process the orders.
- CUse Amazon Managed Streaming for Apache Kafka (Amazon MSK) to stream the orders. Use an
- DUse Amazon DynamoDB Streams to stream the orders. Use an Amazon ECS cluster on AWS
How the community answered
(49 responses)- A45% (22)
- B18% (9)
- C8% (4)
- D29% (14)
Explanation
Amazon Kinesis Data Streams supports ordered and batched data processing. By using a Lambda function with event source mapping and setting MaximumBatchingWindowInSeconds, the developer can batch records over a time window (up to 300 seconds) before sending them to the Aurora MySQL database. This meets the requirement for batch processing and ensures each record is processed before being sent, with low operational overhead.
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