The correct answer is to increase parallelism to 8 because the application’s heap memory per operator is too low. With parallelism set to 4 and only 1 Kinesis Processing Unit (KPU), each operator slot receives a fraction of the 4 GB of heap memory, causing an OutOfMemoryError when processing spikes. Increasing parallelism to 8 forces Kinesis Data Analytics to provision additional KPUs, which adds more total heap memory (each KPU provides 4 GB), resolving the memory pressure. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how parallelism and KPUs interact in Flink-based streaming applications—a common trap is assuming that increasing parallelism alone without considering KPU allocation will fix memory errors. Remember: more parallelism without more KPUs just divides the same small heap into even smaller pieces. Memory tip: “Parallelism without KPUs is just slicing a thin pie thinner.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to a sink. The application is failing with an 'OutOfMemoryError'. The application has parallelism set to 4 and uses 1 Kinesis Processing Unit (KPU). 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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The heap memory per operator is too low; increase parallelism to 8.
With parallelism set to 4 but only 1 KPU, each operator slot receives a fraction of the available heap memory, leading to an OutOfMemoryError. Increasing parallelism to 8 distributes the workload across more slots, but more importantly, it forces Kinesis Data Analytics to allocate additional KPUs (each KPU provides 4 GB of memory), thereby increasing the total heap memory available to the 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.
✗
The application is using too many operators; reduce parallelism to 2.
Why it's wrong here
Reducing parallelism may cause each operator to process more data, worsening OOM.
✓
The heap memory per operator is too low; increase parallelism to 8.
Why this is correct
Higher parallelism allocates more total memory across tasks.
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.
✗
The checkpoint interval is too short; increase it to 5 minutes.
Why it's wrong here
Checkpointing does not affect heap memory.
✗
The buffer timeout is too high; reduce it to 50 ms.
Why it's wrong here
Buffer timeout does not cause OOM.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume increasing parallelism always reduces per-operator memory, but in Kinesis Data Analytics, parallelism is tied to KPU allocation, so increasing parallelism can actually increase total memory by provisioning more KPUs.
Detailed technical explanation
How to think about this question
In Kinesis Data Analytics for Apache Flink, each KPU provides 4 GB of memory (1 GB for heap, 3 GB for off-heap). With parallelism=4 and 1 KPU, each of the 4 parallel operators shares a single 1 GB heap, which is often insufficient for stateful operations or large keyed states. Increasing parallelism to 8 triggers the service to allocate 2 KPUs (since each KPU supports up to 4 parallel subtasks by default), doubling the total heap to 2 GB and allowing each operator slot more memory. This is a common scaling strategy for memory-bound Flink jobs on Kinesis Data Analytics.
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.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The heap memory per operator is too low; increase parallelism to 8. — With parallelism set to 4 but only 1 KPU, each operator slot receives a fraction of the available heap memory, leading to an OutOfMemoryError. Increasing parallelism to 8 distributes the workload across more slots, but more importantly, it forces Kinesis Data Analytics to allocate additional KPUs (each KPU provides 4 GB of memory), thereby increasing the total heap memory available to the application.
What should I do if I get this MLS-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.
About these practice questions
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Variation 1. A company uses Amazon Kinesis Data Analytics for real-time anomaly detection on a stream of IoT sensor data. The application is experiencing high latency. The data volume has doubled. Which action would MOST effectively reduce latency?
hard
✓ A.Increase the Parallelism setting of the Kinesis Data Analytics application
B.Change the record format from JSON to Avro
C.Decrease the retention period of the source stream
D.Increase the number of shards in the source Kinesis stream
Why A: Increasing the parallelism (number of KPUs) in Kinesis Data Analytics allows processing more data in parallel, reducing latency. Changing record format may help but not as much as scaling. Reducing retention is not relevant. Using Lambda adds overhead.
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Question Discussion
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