A company is building a serverless data ingestion pipeline. Data is sent to an Amazon API Gateway, which triggers an AWS Lambda function. The Lambda function transforms the data and writes it to an Amazon Kinesis Data Stream. During peak hours, the API Gateway receives 10,000 requests per second, causing the Lambda function to hit concurrency limits and drop data. Which combination of architectural changes will resolve this issue and ensure no data is lost? (Select TWO)

Answer options:

Configure API Gateway to integrate directly with the Kinesis Data Stream.

Increase the Lambda concurrent execution limit to 20,000.

Move the Lambda function to consume from the Kinesis Data Stream instead of being triggered by API Gateway.

Replace API Gateway with an Application Load Balancer.

Use Amazon SNS instead of Kinesis Data Streams.

Enable caching on the API Gateway.

How to approach this question

Decouple the ingestion layer from the processing layer.

Full Answer

Configure API Gateway to integrate directly with the Kinesis Data Stream., Move the Lambda function to consume from the Kinesis Data Stream instead of being triggered by API Gateway.

To handle high-throughput ingestion, use API Gateway's AWS Service Integration to write directly to Kinesis. Then, use Lambda to consume from Kinesis asynchronously. This decouples ingestion from processing and prevents concurrency limits from causing data loss.

Common mistakes

Trying to solve the problem by just increasing limits, which doesn't fix the architectural flaw.

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