DEA-C01 Data Operations and Support Practice Question
A data engineer is monitoring an Amazon Kinesis Data Analytics application that uses a SQL query to aggregate streaming data. The application is falling behind and the millisBehindLatest metric is increasing. Which action should the engineer take to improve performance?
⚠ Common exam trap
It's easy for candidates to confuse scaling the source (shards) with scaling the processing engine (parallelism), assuming that more data input automatically fixes processing lag, when in fact the bottleneck is the application's compute capacity.
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 setting of the Kinesis Data Analytics application
Increasing the Parallelism setting of the Kinesis Data Analytics application allows the SQL query to process data across more in-application streams and operators concurrently, directly addressing the lag indicated by the rising millisBehindLatest metric. This action scales the compute resources allocated to the application without changing the source stream or the query logic, making it the most direct way to improve throughput for a SQL-based Kinesis Data Analytics application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from SQL to Apache Flink for the analytics application
Why it's wrong here
Flink offers more control but is not a quick fix; the SQL app can be tuned.
- ✗
Increase the number of shards in the source Kinesis stream
Why it's wrong here
If the source is not throttled, more shards won't help; the analytics app is the bottleneck.
- ✓
Increase the Parallelism setting of the Kinesis Data Analytics application
Why this is correct
Higher parallelism increases processing capacity, reducing lag.
- ✗
Decrease the window duration of the SQL query
Why it's wrong here
Smaller windows may process less data per window but do not increase overall throughput.
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Written by Johnson Ajibi, MSc IT Security
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