A company uses Amazon Kinesis Data Streams to ingest telemetry data from IoT devices. The data volume has doubled, and the consumer applications are falling behind, resulting in a high `GetRecords.IteratorAgeMilliseconds` metric. How should a solutions architect resolve this?

Answer options:

Increase the retention period of the stream.

Split the shards in the Kinesis Data Stream.

Merge the shards in the Kinesis Data Stream.

Change the consumer application to use long polling.

How to approach this question

Identify the capacity issue in Kinesis. Throughput is scaled by adding (splitting) shards.

Full Answer

B.Split the shards in the Kinesis Data Stream.✓ Correct

The capacity of a Kinesis Data Stream is determined by the number of shards. If consumers are falling behind (high IteratorAge), you need to increase the stream's capacity by splitting existing shards, which allows for more parallel processing.

Common mistakes

Increasing retention period, which only delays data loss but doesn't fix the bottleneck.

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