DEA-C01 practice question: Throttling, fan-out and replay (1 of 2)
Data Ingestion and Transformation A free question from our original DEA-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A regional bank streams card authorization events into a Kinesis Data Streams stream with 12 shards. Four independent consumers read the full stream: fraud scoring, a risk dashboard, a data lake writer and an audit service.
With standard consumers, all of them share the read throughput of each shard, so the fraud consumer falls behind at peak times. The bank has already sized the stream and does not want to add shards, and every consumer must keep receiving records with low latency.
Which change meets these requirements?
Choose one answer.
AA. Double the shard count to 24 so that the shared read throughput of each shard is spread across more capacity.
BB. Have the fraud consumer increase its GetRecords batch size and poll more often, so that each request returns more records.
CC. Register each consumer as an enhanced fan-out consumer, which gives each one dedicated read throughput on every shard.
DD. Write each event twice from the producer, once to the stream for fraud scoring and once to a second stream for the other three consumers.
Show the answer and explanation
Answer: C
A. Wrong: More shards add read capacity, but the bank has ruled out changing the shard count. Consumers would still share read throughput on each shard, and the cost of the stream would rise.
B. Wrong: Larger polling batches do not raise the per-shard read limit that the consumers share. The standard consumers would still compete for the same read throughput on each shard.
D. Wrong: A second stream removes the sharing, but it doubles the producer work and cost and creates two copies that can drift apart. The bank wants a single stream as the source of record.
Explanation
Standard Kinesis consumers share each shard's read throughput, so more consumers mean less for each. Enhanced fan-out gives each registered consumer its own read throughput on every shard, which fixes the lag without changing the shard count or duplicating the stream. Bigger batches and extra copies of the stream do not change the shared limit.
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