Answer: Use an error retry and exponential backoff mechanism, Decrease the frequency or size of your requests

Use an error retry and exponential backoff mechanism Decrease the frequency or size of your requests You can use PutRecords API call to write multiple data records into a Kinesis data stream in a single call. Each PutRecords request can support up to 500 records. Each record in the request can be as large as 1 MiB, up to a limit of 5 MiB for the entire request, including partition keys. Each shard can support writes up to 1,000 records per second, up to a maximum data write of 1 MiB per second. The response Records array includes both successfully and unsuccessfully processed records. Kinesis Data Streams attempts to process all records in each PutRecords request. A single record failure does not stop the processing of subsequent records. As a result, PutRecords doesn't guarantee the ordering of records. An unsuccessfully processed record includes ErrorCode and ErrorMessage values. ErrorCode reflects the type of error and can be one of the following values: ProvisionedThroughputExceededException or InternalFailure. ProvisionedThroughputExceededException indicates that the request rate for the stream is too high, or the requested data is too large for the available throughput. Reduce the frequency or size of your requests. To address the given use case, you can apply these best practices: Reshard your stream to increase the number of shards in the stream. Reduce the frequency or size of your requests. Distribute read and write operations as evenly as possible across all of the shards in Data Streams. Use an error retry and exponential backoff mechanism. Incorrect options: Merge the shards to decrease the number of shards in the stream Increase the frequency or size of your requests These two options contradict the explanation provided above, so these options are incorrect. Decrease the number of KCL consumers - This option has been added as a distractor. The number of KCL consumers is irrelevant for the given use case since the ProvisionedThroughputExceededException is due to the PutRecords API call being used by the producers.

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