Which combination of changes will resolve these issues?
(Choose two.)
Increase the write capacity units to the DynamoDB table.
Increase the memory available to the Lambda functions.
Increase the payload size from the smart meters to send more data.
Stream the data into an Amazon Kinesis data stream from API Gateway and process the data in batches.
Collect data in an Amazon SQS FIFO queue, which triggers a Lambda function to process each message
Explanations:
Increasing the write capacity units (WCU) for the DynamoDB table will address the ProvisionedThroughputExceededException errors, as it allows the table to handle more writes. This will help prevent throttling due to high request rates.
Increasing the memory for the Lambda functions could improve processing speed by providing more resources for computation, but it does not address the underlying issue of throttling due to high request volumes to DynamoDB or Lambda.
Increasing the payload size may exacerbate the problem as larger payloads may require more processing time and memory. It will not solve the throttling issues.
Using Amazon Kinesis data streams will allow for batch processing of data, which can reduce the number of individual Lambda invocations. This can alleviate the TooManyRequestsException errors and enable more efficient handling of data.
Collecting data in an SQS FIFO queue will allow for message ordering, but it does not address the root cause of the throttling errors. Using SQS does not inherently reduce the request load or improve throughput to DynamoDB or Lambda.
I strategize that the answer is:
Increase the write capacity units to the DynamoDB table.
I scheme that the answer is:
Increase the write capacity units to the DynamoDB table.