DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Analytics for real-time anomaly detection on clickstream data. The application uses a sliding window of 1 minute. The data engineer notices that the application is producing incorrect results because late-arriving records are not being handled properly. What should the data engineer do to ensure late records are included in the window calculations?
⚠ Common exam trap
Watch out — candidates often confuse stream retention (how long data is stored) with watermark delay (how long the application waits for late events), leading them to incorrectly choose option D.
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 watermark delay in the Kinesis Data Analytics application to allow more time for late records.
Kinesis Data Analytics uses watermarks to track event time progress and determine when to finalize window calculations. Increasing the watermark delay allows the application to wait longer for late-arriving records before closing the window, ensuring they are included in the aggregation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Kinesis Data Firehose to buffer the data and then send to Kinesis Data Analytics.
Why it's wrong here
Firehose buffers and delivers records to destinations such as S3 or Redshift; it cannot alter window semantics inside a Kinesis Data Analytics application. Late records are handled by configuring allowed lateness on the in-application sliding window, not by inserting a delivery buffer upstream. Firehose suits ingestion-to-storage pipelines, not stream processing corrections.
- ✓
Increase the watermark delay in the Kinesis Data Analytics application to allow more time for late records.
Why this is correct
Increasing the watermark delay lets the application tolerate out-of-order events by extending how long it waits before closing a window, so late-arriving clickstream records still fall inside the 1-minute sliding window and are included in the anomaly calculations. This directly satisfies the requirement to handle late data correctly.
- ✗
Increase the window size from 1 minute to 2 minutes.
Why it's wrong here
Widening the sliding window changes the aggregation period but does not admit records arriving after the window closes, so late data is still dropped. It is tempting because a longer window appears to give records more time, and would be correct if the requirement were to smooth results over a broader time range.
- ✗
Increase the retention period of the Kinesis stream to 7 days.
Why it's wrong here
Stream retention governs how long Kinesis stores records for consumers to read; it does not alter the application's window semantics, so late records are still excluded from closed windows. It is tempting because retention sounds like it addresses lateness, and would be correct if the problem were replaying older data rather than window inclusion.
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Same concept, more angles
1 more way this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Configure a Kinesis Firehose delivery stream to buffer the late events.
- B.Increase the shard count of the source Kinesis stream.
- ✓ C.Set the allowed_lateness parameter in the application's windowed aggregation.
- D.Increase the RecordColumn count in the input stream mapping.
Why C: In Amazon Kinesis Data Analytics (KDA), late-arriving events are handled by the allowed_lateness parameter in windowed aggregations. Setting this parameter allows the application to accept and process events that arrive after the window has closed, up to the specified lateness threshold. This directly addresses the issue of dropped late events by extending the window's acceptance period.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.