DEA-C01 Data Operations and Support Practice Question
A company is ingesting streaming data from thousands of IoT devices into Amazon Kinesis Data Streams. The data is processed by a Kinesis Data Analytics application. Recently, the application started reporting high iterator age (millisBehindLatest). Which action would BEST reduce the iterator age?
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 number of shards in the Kinesis stream.
Increasing the number of shards increases the stream's throughput capacity, allowing the Kinesis Data Analytics application to consume data faster and reduce the iterator age (millisBehindLatest). Option A is incorrect: decreasing the data retention period does not improve processing speed; it only reduces the time data is stored. Option B is incorrect: increasing retention also does not affect processing speed. Option C is incorrect: the record size limit is fixed (1 MB) and cannot be increased; increasing shards is the appropriate scaling action.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the data retention period of the Kinesis stream.
Why it's wrong here
Shortening retention discards data sooner and can worsen the backlog; it does nothing to accelerate the analytics application's consumption of shards. It is tempting because retention is frequently adjusted on Kinesis streams, and reducing it would be correct when storage cost or data-minimisation requirements, not iterator age, drive the change.
- ✗
Increase the data retention period of the Kinesis stream.
Why it's wrong here
Retention controls how long records remain readable, not how quickly the analytics application reads them, so iterator age is unaffected. It is tempting because retention is a common Kinesis tuning lever, and extending it would be correct when consumers must replay or reprocess older data after an outage.
- ✗
Increase the record size limit in the Kinesis stream.
Why it's wrong here
Record size limits govern individual put payloads, not how fast consumers drain shards, so raising them leaves millisBehindLatest unchanged. It is tempting because larger records reduce per-record overhead, and that setting would be correct when throughput is constrained by API call volume rather than consumer processing.
- ✓
Increase the number of shards in the Kinesis stream.
Why this is correct
Iterator age grows when consumers cannot keep pace with incoming records. Adding shards increases the stream's parallel processing capacity, letting the Kinesis Data Analytics application consume records faster and reduce millisBehindLatest, unlike scaling the application alone.
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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.