DEA-C01 Data Operations and Support Practice Question
A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application is experiencing high latency and checkpoint failures. Which THREE actions should the data engineer take to improve performance and reliability? (Choose three.)
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
Options A, B, and C are correct. Option A: Increasing parallelism improves throughput by distributing workload across more resources. Option B: Using event time processing helps handle out-of-order data and can reduce latency and checkpoint failures by allowing more accurate watermarks. Option C: Increasing the checkpoint interval reduces the frequency of checkpoint operations, which can reduce checkpoint failures under high load. Option D is incorrect because decreasing parallelism reduces throughput, worsening latency. Option E is incorrect because disabling checkpointing removes fault tolerance and does not improve performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the parallelism of the Flink application
Why this is correct
Higher parallelism improves throughput.
- ✓
Configure the application to use event time processing instead of processing time
Why this is correct
Event time handles out-of-order data better.
- ✓
Increase the checkpoint interval to reduce the frequency of checkpoints
Why this is correct
Less frequent checkpoints reduce overhead.
- ✗
Decrease the parallelism to reduce resource contention
Why it's wrong here
Decreasing parallelism may increase latency.
- ✗
Disable checkpointing to avoid checkpoint failures
Why it's wrong here
Checkpointing is essential for fault tolerance.
Go deeper
Related to this question
About these practice questions
Courseiva writes every DEA-C01 question from scratch — 1,711 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways 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 data engineer is monitoring an Amazon Kinesis Data Analytics for Apache Flink application that processes streaming data. The application is falling behind (increasing 'MillisBehindLatest') and the CPU utilization of the Flink task managers is consistently above 80%. Which THREE actions should the engineer take to improve performance? (Choose THREE.)
medium- ✓ A.Increase the number of shards in the Kinesis data stream.
- B.Decrease the checkpoint interval to reduce state size.
- ✓ C.Enable auto-scaling for the Flink application.
- D.Decrease the number of task managers to reduce CPU contention.
- ✓ E.Increase the Flink application's parallelism.
Why A: Increasing the number of shards in the Kinesis data stream (Option A) directly increases the ingestion capacity and parallelism source for the Flink application. With more shards, the application can read data from more partitions concurrently, reducing the backlog indicated by 'MillisBehindLatest'. This is a fundamental scaling action for Kinesis-based Flink applications.
Variation 2. 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?
hard- A.Decrease the batch size in the Flink application.
- B.Switch the source stream to use GZIP compression.
- ✓ C.Increase the parallelism of the Flink application.
- D.Increase the retention period of the Kinesis stream.
Why C: Increasing parallelism in the Flink application allows more tasks to process data concurrently, which can reduce the 'millisBehindLatest' metric (lag). Decreasing batch size (Option A) would reduce throughput per task, worsening lag. Switching to GZIP compression (Option B) may reduce data size but adds decompression overhead and does not directly address processing parallelism. Increasing the retention period (Option D) does not affect processing speed; it only keeps data longer in the stream.
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.