DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Analytics (now Managed Service for Apache Flink) to run a Flink application on streaming data. The application fails with 'OutOfMemoryError: Java heap space'. The data volume is 10 MB/s. What is the most likely cause and solution?
⚠ Common exam trap
The trap here is that candidates often misdiagnose an OOM as a record size issue (Option A) or a checkpointing problem (Option B), when in fact the root cause is insufficient parallelism to handle the sustained throughput, which is a common scaling pitfall in Flink on Kinesis Data Analytics.
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
✓
The application's Parallelism is too low; increase the number of Parallelism and KPUs.
The OutOfMemoryError in a Flink application on Amazon Kinesis Data Analytics (Managed Service for Apache Flink) is most likely due to insufficient parallelism to handle the 10 MB/s data volume. Increasing parallelism distributes the workload across more KPUs (Kinesis Processing Units), reducing memory pressure per operator and preventing heap exhaustion. Option D directly addresses this by scaling resources to match throughput.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The data contains records larger than 1 MB; split records into smaller chunks.
Why it's wrong here
Large records may cause issues, but OOM is typically due to resource constraints.
- ✗
Checkpointing is enabled too frequently; reduce checkpoint interval.
Why it's wrong here
More frequent checkpoints increase overhead but not OOM.
- ✗
The Flink application is not suitable for 10 MB/s throughput; use Kinesis Data Firehose instead.
Why it's wrong here
Flink can handle much higher throughput with proper configuration.
- ✓
The application's Parallelism is too low; increase the number of Parallelism and KPUs.
Why this is correct
Low parallelism causes data to accumulate in operator buffers, leading to OOM.
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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.