- A
Consolidate small input files into fewer larger files
Why wrong: This can improve performance but may not be enough to reduce runtime by 2 hours.
- B
Enable EMR Managed Scaling
Managed Scaling dynamically adds resources to meet deadlines.
- C
Increase the memory of each node by using r5 instances
Why wrong: Memory may not be the bottleneck; compute parallelism is key.
- D
Use Spot Instances for all core nodes
Why wrong: Spot instances can be reclaimed, causing delays.
Quick Answer
The answer is enabling EMR Managed Scaling because it dynamically adjusts cluster resources in real time based on the workload, directly addressing the 8-hour runtime bottleneck for a 100 TB Spark pipeline. EMR Managed Scaling uses CloudWatch metrics and YARN container utilization to automatically add or remove core and task nodes, ensuring that Spark jobs get the parallelism they need without manual intervention. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of cost-effective, automated scaling strategies for large-scale data processing—a common scenario where candidates mistakenly choose spot instances or memory tweaks. The trap is that spot instances risk interruptions, while adding memory per node doesn’t fix a parallelism bottleneck; consolidating files reduces overhead but isn’t the primary issue here. Memory tip: think “auto-scale, not manual tinker” to remember that EMR Managed Scaling optimizes runtime by matching resources to demand in real time.
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 is running a data pipeline that uses Amazon EMR with Spark to process 100 TB of data daily. The pipeline must complete within 6 hours. Currently, it takes 8 hours. Which optimization will most likely reduce the runtime?
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 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
Enable EMR Managed Scaling
Option D is correct because enabling EMR Managed Scaling automatically adjusts cluster resources based on workload, which can reduce runtime. Option A is wrong because using spot instances may cause interruptions; Option B is wrong because more memory per node may not help if the bottleneck is parallelism; Option C is wrong because consolidating data into fewer files can reduce overhead but may not be the main 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.
- ✗
Consolidate small input files into fewer larger files
Why it's wrong here
This can improve performance but may not be enough to reduce runtime by 2 hours.
- ✓
Enable EMR Managed Scaling
Why this is correct
Managed Scaling dynamically adds resources to meet deadlines.
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.
- ✗
Increase the memory of each node by using r5 instances
Why it's wrong here
Memory may not be the bottleneck; compute parallelism is key.
- ✗
Use Spot Instances for all core nodes
Why it's wrong here
Spot instances can be reclaimed, causing delays.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 EMR Managed Scaling — Option D is correct because enabling EMR Managed Scaling automatically adjusts cluster resources based on workload, which can reduce runtime. Option A is wrong because using spot instances may cause interruptions; Option B is wrong because more memory per node may not help if the bottleneck is parallelism; Option C is wrong because consolidating data into fewer files can reduce overhead but may not be the main 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.
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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Last reviewed: Jun 20, 2026
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.
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