DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Analytics to process real-time data. The application needs to aggregate data over a 10-minute window. The team notices that late-arriving events are being dropped. Which configuration should they adjust?
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
✓
Set the allowed_lateness parameter in the application's windowed aggregation.
Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) provides an `allowed_lateness` parameter in windowed aggregations to handle late-arriving events. Option A is incorrect because Kinesis Firehose is used for data delivery and buffering, not for handling late events within Kinesis Data Analytics; the application itself must manage lateness. Option B is incorrect because increasing shard count increases parallelism and throughput but does not affect how late events are treated; they will still be dropped unless a lateness tolerance is set. Option D is incorrect because `RecordColumn` count defines the schema of the input stream and has no impact on late-arriving event handling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure a Kinesis Firehose delivery stream to buffer the late events.
Why it's wrong here
Kinesis Firehose is used for data delivery and buffering, not for handling late events within Kinesis Data Analytics; the application itself must manage lateness.
- ✗
Increase the shard count of the source Kinesis stream.
Why it's wrong here
Increasing shard count increases parallelism and throughput but does not affect how late events are treated; they will still be dropped unless a lateness tolerance is set.
- ✓
Set the allowed_lateness parameter in the application's windowed aggregation.
Why this is correct
Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) provides an `allowed_lateness` parameter in windowed aggregations to handle late-arriving events.
- ✗
Increase the RecordColumn count in the input stream mapping.
Why it's wrong here
`RecordColumn` count defines the schema of the input stream and has no impact on late-arriving event handling.
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