DEA-C01 Data Operations and Support Practice Question
A data engineer is monitoring an Amazon Kinesis Data Analytics application that processes real-time clickstream data. The application uses a Flink application with multiple operators. The engineer notices that the 'millisBehindLatest' metric is increasing steadily. Which action is MOST likely to reduce the lag?
⚠ Common exam trap
DEA-C01 often tests the interpretation of millisBehindLatest and the appropriate scaling action, and candidates may mistakenly choose options that alter data format or retention rather than addressing processing 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 of the Flink application.
Increasing the parallelism of the Flink application allows more parallel subtasks to process the incoming data, thereby increasing throughput and reducing the lag (millisBehindLatest). This is the most direct way to scale the application to handle higher load. Other options do not address the root cause of increasing lag.
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 batch size in the Flink application.
Why it's wrong here
Reducing batch size lowers throughput per checkpoint rather than relieving sustained backpressure, so millisBehindLatest keeps climbing. Batching is tuned for latency-versus-throughput trade-offs in steady pipelines, not for a consumer that cannot keep pace with incoming shard volume.
- ✗
Switch the source stream to use GZIP compression.
Why it's wrong here
GZIP compression reduces network bytes but Flink must decompress each record, adding CPU per record and typically worsening millisBehindLatest when operators are the bottleneck. Compression suits bandwidth-constrained producers, not consumer lag; increasing parallelism or shard count addresses processing throughput.
- ✓
Increase the parallelism of the Flink application.
Why this is correct
Raising Flink parallelism distributes the lagging operators across more subtasks, so each processes a smaller share of the incoming clickstream and drains the backlog faster. This directly addresses the steadily rising millisBehindLatest by adding processing capacity, provided the source shards and downstream sinks can absorb the extra throughput.
- ✗
Increase the retention period of the Kinesis stream.
Why it's wrong here
Retention governs how long records remain readable, not how fast they are consumed, so extending it leaves millisBehindLatest unchanged. It is tempting because longer retention helps reprocessing after failures or for late consumers; reducing lag requires more consumer throughput, such as higher parallelism or additional shards.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.