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Answer: Use a random partition key to distribute the ingested records.
Option A is **CORRECT** because using a random partition key helps ensure that incoming records are evenly distributed across all available shards in the Kinesis Data Stream. Hot shards occur when certain shards receive disproportionate amounts of data, often due to poorly selected partition keys. By introducing randomness in partition key assignment, the data load is balanced, avoiding overloading specific shards. This is an effective and low-effort solution to distribute write throughput evenly, minimizing throttling without needing to scale up resources or alter the producer’s behavior.
Author: Ritesh Yadav
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Question 55/60
A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.
The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.
How should the data engineer resolve the throttling issue?
A
Use a random partition key to distribute the ingested records.
B
Increase the number of shards in the data stream. Distribute the records across the shards.
C
Limit the number of records that are sent each second by the producer to match the capacity of the stream.
D
Decrease the size of the records that the producer sends to match the capacity of the stream.
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