What should the solutions architect do to accomplish this goal?
Modify the application to write to the DAX cluster. Configure the DAX cluster to write to the DynamoDB table when the maintenance window is complete.
Enable Amazon DynamoDB Streams for the DynamoDB table. Modify the application to write to the stream. Configure the stream to load the data when the maintenance window is complete.
Convert the application to an AWS Lambda function. Configure the Lambda function runtime to be longer than the maintenance window. Create an Amazon CloudWatch alarm to monitor Lambda timeouts.
Modify the application to write the document name and URLs to an Amazon Simple Queue Service (Amazon SQS) queue. Create an AWS Lambda function to read the SQS queue and write to DynamoDB.
Explanations:
Modifying the application to write to the DAX cluster and later write to DynamoDB after the maintenance window does not resolve the issue during the maintenance window. DAX is unavailable during maintenance, so writes would fail.
Enabling DynamoDB Streams allows capturing changes to the table but does not solve the issue of processing during the maintenance window. It only records changes, which does not help with writing data during the maintenance window.
Converting the application to a Lambda function with a longer runtime does not address the root cause of the issue, which is the DAX cluster being unavailable during maintenance. Lambda timeouts will still occur if the function cannot access DAX or DynamoDB.
Writing document names and URLs to an SQS queue ensures the data is temporarily stored while DAX is unavailable. A Lambda function can then process the queue and write to DynamoDB after the maintenance window ends, ensuring continuous operation.
As I recall, the answer is:
Modify the application to write the document name and URLs to an Amazon Simple Queue Service (Amazon SQS) queue. Create an AWS Lambda function to read the SQS queue and write to DynamoDB.