The correct answer is to increase the executor memory in Spark configuration. This resolves the EMR Spark executor memory error because the job fails when executors run out of heap space while processing large partitions, and raising the spark.executor.memory parameter allocates more RAM per executor to handle the data load without spilling to disk or throwing OutOfMemory errors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of Spark resource tuning on Amazon EMR, often appearing as a trap where candidates confuse driver memory with executor memory—remember that executors do the heavy lifting for data transformations, not the driver. A common mistake is choosing to reduce nodes, which actually shrinks aggregate memory, or switching to Parquet, which optimizes storage but does not fix an active memory shortage. Memory tip: think “executors execute the work, so feed them first.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Exhibit
Refer to the exhibit.
```
$ cat /var/log/syslog | grep "OutOfMemory"
2024-01-15 10:30:45 ERROR OutOfMemoryError: Java heap space
at org.apache.spark.sql.catalyst.expressions.GenerateMutableProjection.apply(Unknown Source)
```
Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the executor memory in Spark configuration.
Option B is correct because increasing the executor memory in Spark configuration can handle larger data. Option A (fewer nodes) reduces resources; Option C (Parquet) may help but not directly address memory; Option D (increase driver memory) may not help if executors are the issue.
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.
✗
Reduce the number of worker nodes.
Why it's wrong here
Fewer nodes reduce total memory.
✗
Convert the input data to Parquet format.
Why it's wrong here
Parquet is efficient but not a direct fix for OOM.
✓
Increase the executor memory in Spark configuration.
Why this is correct
More memory per executor prevents heap overflow.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the driver memory.
Why it's wrong here
Driver memory is for coordination, not data processing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the executor memory in Spark configuration. — Option B is correct because increasing the executor memory in Spark configuration can handle larger data. Option A (fewer nodes) reduces resources; Option C (Parquet) may help but not directly address memory; Option D (increase driver memory) may not help if executors are the issue.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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