A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management…

If a Kinesis producer (such as KPL) sends a record and experiences a network-related timeout, it might retry the send operation even if the initial attempt succeeded on the Kinesis Data Stream side, leading to the same record being written twice.

A high `IteratorAgeMilliseconds` metric indicates that the consumer is falling behind in processing records but does not directly cause duplication; it signifies processing latency.

Changes in the number of shards (scaling up or down) or rebalancing of record processors within the Kinesis Client Library (KCL) can cause a record processor to lose its lease on a shard and another instance to take over, potentially starting from an earlier checkpoint and reprocessing already consumed records.

Setting `AggregationEnabled` to true in KPL aggregates multiple small records into larger payloads for efficient transmission but does not cause duplicates, as KCL correctly disaggregates them upon consumption.

Setting the `max_records` configuration property too high in KCL might fetch more records in a batch but does not inherently cause duplication; KCL's checkpointing mechanism is designed to prevent reprocessing of already successfully processed records.