Topic 1 Question 205
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?
Use a random partition key to distribute the ingested records.
Increase the number of shards in the data stream. Distribute the records across the shards.
Limit the number of records that are sent each second by the producer to match the capacity of the stream.
Decrease the size of the records that the producer sends to match the capacity of the stream.
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コメント(2)
- 正解だと思う選択肢: A
A: Sí, usar una clave de partición aleatoria distribuirá uniformemente los registros entre los shards, reduciendo cuellos de botella en shards "calientes". B: No, aumentar shards no soluciona la desproporción si la clave sigue causando concentración.
👍 2italiancloud20252025/02/18 - 正解だと思う選択肢: B
Amazon Kinesis Data Streams uses shards to distribute data, and each shard has a fixed throughput limit. If certain shards receive significantly more data than others (hot shards), they will experience throttling. To resolve this issue:
Increase the number of shards – This increases the overall capacity of the stream. Distribute records more evenly across shards – This can be done by modifying the partition key strategy so that data is spread more evenly.
👍 2JekChong2025/02/21
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