Which combination of steps will reduce the latency?
(Choose three.)
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer.
Increase the ParallelizationFactor setting in the Lambda event source mapping.
Configure reserved concurrency for the Lambda function that processes the logs.
Increase the batch size in the Kinesis data stream.
Turn off the ReportBatchItemFailures setting in the Lambda event source mapping.
Increase the number of shards in the Kinesis data stream.
Explanations:
Enhanced fan-out allows multiple consumers to read from a Kinesis stream concurrently with low latency. This reduces the overall latency of processing logs.
Increasing the ParallelizationFactor allows the Lambda function to process more records in parallel, improving throughput and reducing processing delays.
Reserved concurrency ensures a specific number of instances of a Lambda function can run concurrently, but it doesn’t directly address latency issues in this scenario.
Increasing the batch size would actually increase the size of each batch processed by the Lambda, which might lead to longer processing times and higher latency.
Turning off ReportBatchItemFailures would stop Lambda from reporting partial failures, but it won’t directly reduce latency; it may result in fewer retries and error reports.
Increasing the number of shards in the Kinesis stream improves throughput by allowing more parallel processing, which can reduce bottlenecks and reduce latency.
In my opinion, the answer is:
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer.
I evaluate that the answer is:
Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer.