Tick-Bank is a privately held Internet retailer of both physical and digital products founded in 2008. The company has more than six-million clients worldwide. Tick-Bank aims to serve as a connection between digital content makers and affiliate dealers, who then promote them to clients. Tick-Bank`s technology aids in payments, tax calculations and a variety of customer service tasks. Tick-Bank assists in building perceptibility and revenue making opportunities for entrepreneurs.
Tick-Bank runs multiple java based web applications running on windows based EC2 machines in AWS managed by internal IT Java team, to serve various business functions. Tick-Bank is looking to enable web-site traffic analytics thereby understanding user navigational behavior, preferences, and other click related info. Tick-Bank uses event based streaming, based on Kinesis Stream to address data integration and uses producer library to integrate events.
Tick-Bank has multiple kinesis data streams supporting various business processes and uses enhanced fan-out consumers to fulfill the processing of data. Tick-Bank proposes KPL library to produce the data stream while KCL library to consume the data. A lot of kinesis throughput exceptions are thrown. Which of the following option can help us to resolve the issue?

Answer options:

A.Increase the read and write throughput of Kinesis Application’s DynamoDB table
B.Configure Enhanced fan-out consumers to process the data
C.Frequent check-pointing on thestream that is composed of many shards
D.Configure Sharedfan-out consumers to process the data

Answer: A
Option A is correct - Increase the read and write throughput of DynamoDB table pertaining to a specific kinesis application to prevent throughput exceptions. Tracking Amazon Kinesis Data Streams Application State is managed by a DynamoDB table for each stream.
Besides For each Amazon Kinesis Data Streams application, the KCL uses a unique Amazon DynamoDB table to keep track of the application`s state.
If the Amazon DynamoDB table for your Amazon Kinesis Data Streams application does not exist when the application starts up, one of the workers creates the table and calls the describeStream method to populate the table.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-record-processor-ddb.html
Option B is incorrect - enhanced fan-out consumers is applied to provide a read rate of 2 MiB/sec per shard per consumer. this helps provide consistent performance in the application but does not help with errors.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-record-processor-ddb.html
Option C is incorrect - Frequent checkpointing is one of the root cause for kinesis throughput exception errors
Option D is incorrect - shared fan-out consumers is applied to provide a maximum read rate of 2 MiB/sec per shard.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-record-processor-ddb.html