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 runs a critical data pipeline using Apache Spark on Amazon EMR. The pipeline reads data from Amazon S3, performs complex transformations, and writes results back to S3. The job runs every hour and must complete within 30 minutes. Recently, the job has been taking longer and occasionally failing due to executor losses. The team suspects memory pressure. Which action should the team take to improve stability and performance without increasing cost?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Spark dynamic allocation to adjust executors based on workload.
Option C is correct because enabling dynamic allocation allows Spark to release idle executors and request more when needed, reducing memory pressure. Option A is wrong because increasing the number of core nodes increases cost. Option B is wrong because increasing executor memory per node may cause YARN containers to fail if the instance memory is exceeded. Option D is wrong because reducing shuffle partitions may reduce parallelism and increase memory per task.
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.
✗
Increase the spark.executor.memory setting to allocate more memory per executor.
Why it's wrong here
If the instance does not have enough memory, this can cause container failures.
✗
Increase the number of core nodes in the EMR cluster.
Why it's wrong here
Adding nodes increases cost.
✗
Decrease the number of shuffle partitions (spark.sql.shuffle.partitions) to reduce overhead.
Why it's wrong here
Fewer partitions can lead to larger partitions and more memory pressure.
✓
Enable Spark dynamic allocation to adjust executors based on workload.
Why this is correct
Dynamic allocation helps utilize resources efficiently and prevents over-allocation.
Related concept
Read the scenario before looking for a memorised answer.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
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: Enable Spark dynamic allocation to adjust executors based on workload. — Option C is correct because enabling dynamic allocation allows Spark to release idle executors and request more when needed, reducing memory pressure. Option A is wrong because increasing the number of core nodes increases cost. Option B is wrong because increasing executor memory per node may cause YARN containers to fail if the instance memory is exceeded. Option D is wrong because reducing shuffle partitions may reduce parallelism and increase memory per task.
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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This MLS-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 MLS-C01 exam.
Question Discussion
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