Exam AWS Certified Solutions Architect - Professional topic 1 question 964 discussion

https://repost.aws/knowledge-center/kinesis-readprovisionedthroughputexceeded Follow Data Streams best practices To mitigate ReadProvisionedThroughputExceeded exceptions, apply these best practices: • Reshard your stream to increase the number of shards in the stream. • Use consumers with enhanced fan-out. For more information about enhanced fan-out, see Developing custom consumers with dedicated throughput (enhanced fan-out). • Use an error retry and exponential backoff mechanism in the consumer logic if ReadProvisionedThroughputExceeded exceptions are encountered. For consumer applications that use an AWS SDK, the requests are retried by default.

B is wrong, KPL is aimed to help devs to achieve high write throughtput into Kinesis data stream. Current bottleneck is at read side not write side. D is wrong. Each shard can support up to five read transactions per second. When we increase shard we not only increase write but also increate read. https://docs.aws.amazon.com/streams/latest/dev/service-sizes-and-limits.html F is wrong, Dynamic partitioning is for Kinesis Firehose, "Partitioning your data minimizes the amount of data scanned, optimizes performance, and reduces costs of your analytics queries on Amazon S3", see https://docs.aws.amazon.com/firehose/latest/dev/dynamic-partitioning.html.

ABC: A- Reshard your stream to increase the number of shards B- adjust the polling frequency C- enhanced fan-out feature https://repost.aws/knowledge-center/kinesis-readprovisionedthroughputexceeded

ace are the correct ans

Corecct ACE

ACE are the ones. A: Increase shards. C: Fan-Out. E: Exponential Backoff Mechanism

I go with BCD ReadProvisionedThroughputExceeded https://aws.amazon.com/premiumsupport/knowledge-center/kinesis-readprovisionedthroughputexceeded/ Follow data flow best practices to mitigate ReadProvisionedThroughputExceeded exceptions, use the following best practices: * Resize your stream to increase the number of partitions in the stream. * Reduce the size of GetRecords requests. You can do this by configuring the limit setting or by reducing the frequency of GetRecords requests. * Distribute read and write operations as evenly as possible across all partitions in Data Streams. * Use consumer applications with the enhanced distribution. For more information on enhanced distribution, see, Developing Custom Consumer Applications with Dedicated Throughput (Enhanced Distribution). * Use error retry and exponential backoff mechanism in consumer logic in case of ReadProvisionedThroughputExceeded exceptions. For consumer applications that use an AWS SDK, requests are retried by default.