DEA-C01 Data Ingestion and Transformation Practice Question
A company runs an e-commerce platform that generates clickstream data from millions of users. The data is ingested into Amazon Kinesis Data Streams with a shard count of 10. The data is then consumed by a Kinesis Data Analytics application that runs SQL queries to aggregate metrics in real time. Recently, the application has been falling behind, and the stream's iterator age metric is increasing. The data volume has doubled over the past month. The application currently uses a single Kinesis Data Analytics application with parallelism of 1. Which action should the data engineer take to improve the processing rate and reduce the iterator age without losing data or causing duplicates?
⚠ Common exam trap
A common mix-up: candidates assume increasing shard count (Option C) is the only way to handle higher data volume, but they overlook that the processing application's parallelism must also scale to consume the additional shards, otherwise the bottleneck shifts to the consumer.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the parallelism of the Kinesis Data Analytics application to match the number of shards.
Kinesis Data Analytics (KDA) processes data from each shard in a stream using one or more parallel operators. With a parallelism of 1, the application uses only a single processing thread, which cannot keep up with the doubled data volume across 10 shards. By increasing parallelism to match the shard count (10), KDA can read from all shards concurrently, distributing the processing load and reducing the iterator age without data loss or duplicates, as KDA manages checkpointing and exactly-once semantics internally.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the Kinesis Data Analytics application to use a Kinesis Data Firehose delivery stream as the source.
Why it's wrong here
Firehose is not a real-time analytics solution; it delivers data to destinations, not for streaming SQL.
- ✗
Reduce the retention period of the Kinesis Data Streams to 24 hours.
Why it's wrong here
Reducing retention does not speed up processing; it may cause data loss.
- ✗
Increase the number of shards in the Kinesis Data Streams to 20.
Why it's wrong here
More shards without increasing parallelism will not help; the application still processes one shard at a time.
- ✓
Increase the parallelism of the Kinesis Data Analytics application to match the number of shards.
Why this is correct
Higher parallelism allows concurrent processing of multiple shards.
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