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Data Ingestion and Transformation →hardMultiple ChoiceObjective-mapped

Flink OutOfMemoryError: Increase Parallelism to Fix

This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

A
The data contains records larger than 1 MB; split records into smaller chunks.
Why wrong: Large records may cause issues, but OOM is typically due to resource constraints.
B
Checkpointing is enabled too frequently; reduce checkpoint interval.
Why wrong: More frequent checkpoints increase overhead but not OOM.
C
The Flink application is not suitable for 10 MB/s throughput; use Kinesis Data Firehose instead.
Why wrong: Flink can handle much higher throughput with proper configuration.
D
The application's Parallelism is too low; increase the number of Parallelism and KPUs.
Low parallelism causes data to accumulate in operator buffers, leading to OOM.

Full breakdown with real-world context →

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.

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.

✗
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

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.

Detailed technical explanation

How to think about this question

Under the hood, Flink's memory management divides heap into managed memory (for operator state) and framework heap (for network buffers and user code). At 10 MB/s, if parallelism is too low, each subtask processes a larger share of data, causing the framework heap to overflow. Increasing parallelism and KPUs allocates more total heap and distributes the data load, preventing OOM. In real-world scenarios, monitoring the 'KinesisProcessingUnits' metric and adjusting parallelism based on 'bytesPerSecond' per shard is critical for stable operation.

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

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

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.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: 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.

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.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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