How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.

Station A, which has 10 sensors

Station B, which has five sensors

These weather stations were placed by onsite subject-matter experts.

Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.

Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.

How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?
A . Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
B . Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
C . Modify the partition key to use the sensor ID instead of the station name.
D . Reduce the number of sensors in Station A from 10 to 5 sensors.

View Answer

Answer: C

Explanation:

https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding.html

"Splitting increases the number of shards in your stream and therefore increases the data capacity of the stream. Because you are charged on a per-shard basis, splitting increases the cost of your stream"