AWS Certified Data Engineer Associate DEA-C01 Practice Question

An ecommerce platform streams purchase events to an Amazon Kinesis Data Stream that contains three shards. A Lambda function is configured as the only consumer through an event source mapping. CloudWatch shows the IteratorAge metric growing to several minutes even though the function successfully processes each batch in less than 200 ms. The team must reduce the lag without changing code or adding shards. Which action should the data engineer take?

Reduce the BatchSize value to invoke the function with fewer records more frequently.
Enable enhanced fan-out on the stream and register the Lambda function as an enhanced consumer.
Increase the ParallelizationFactor setting on the event source mapping so multiple batches from each shard are processed concurrently.
Increase the Lambda function's memory allocation to provide more CPU and shorten runtime.

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Answer Description

The correct action is to increase the ParallelizationFactor. Lambda polls each Kinesis shard and, by default, invokes only one concurrent function per shard. When IteratorAge is high despite fast function execution, the bottleneck is the rate of processing. Increasing the ParallelizationFactor from the default of 1 (up to 10) allows Lambda to process multiple batches from each shard concurrently, which directly increases throughput and reduces IteratorAge. Reducing the BatchSize would trigger more frequent, smaller invocations but would not increase the number of concurrent executions per shard, so it would not solve the throughput bottleneck. While Lambda can be configured as an enhanced fan-out (EFO) consumer, EFO's main benefit is providing dedicated throughput to each consumer, which is most useful when multiple applications are reading from the same stream. For a single consumer, increasing the ParallelizationFactor is a more direct and simpler solution. Increasing the Lambda function's memory would not help, because the function's 200 ms runtime is already very low, indicating that compute resources are not the limiting factor.

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