DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics for SQL applications uses a fixed parallelism model where each shard in the source stream is mapped to an in-application stream, but the application's overall parallelism (number of in-application streams and operators) is controlled by the Parallelism setting. Increasing this value allows the application to distribute the SQL query processing across more logical partitions, effectively scaling out the compute resources. Under the hood, this is similar to increasing the number of task slots in Apache Flink or the number of executors in Spark Streaming, enabling better utilization of the underlying Kinesis Processing Units (KPUs).
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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