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
This adds buffering but does not solve the late-data problem.
- ✓
Increase the watermark delay in the Kinesis Data Analytics application to allow more time for late records.
Why this is correct
Watermark delay controls how long the application waits for late data.
- ✗
Increase the window size from 1 minute to 2 minutes.
Why it's wrong here
This changes the window, but does not specifically handle late data.
- ✗
Increase the retention period of the Kinesis stream to 7 days.
Why it's wrong here
Retention period does not affect window calculations.
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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: 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.
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.