- A
Migrate the ETL jobs to Amazon EMR with Apache Spark
Why wrong: EMR adds management overhead and complexity.
- B
Use AWS Glue Flex execution to allocate resources dynamically
Flex execution provides flexible resources that adapt to workload, optimizing cost and performance.
- C
Increase the number of DPUs (Data Processing Units) for all jobs
Why wrong: Increases cost during low-volume periods and may not be efficient.
- D
Split the jobs into smaller steps and run them sequentially
Why wrong: Does not solve the memory issue and increases complexity.
Quick Answer
The answer is to use AWS Glue Flex execution to allocate resources dynamically. This is correct because Flex execution allows AWS Glue to handle variable memory demands by leveraging spare capacity in the AWS cloud, automatically scaling resources up during data volume spikes and down during low-volume periods, which directly addresses the failing jobs without over-provisioning. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of cost-optimized resource management for ETL workloads, often appearing as a trap where candidates mistakenly choose to increase DPU (wasteful for variable loads) or migrate to Amazon EMR (unnecessary complexity). The key insight is that Flex execution is purpose-built for unpredictable, bursty memory needs while maintaining cost efficiency. Memory tip: think “Flex for flux” — when memory demands fluctuate, Flex execution flexes to match.
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 using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are currently failing due to insufficient memory. The data volume varies, with occasional spikes. Which solution should be used to handle the variable memory requirements efficiently?
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
Use AWS Glue Flex execution to allocate resources dynamically
AWS Glue Flex execution allows jobs to use flexible resources that can handle varying memory needs, and it is cost-effective for variable workloads. Option A (increasing DPU) would waste resources during low volume. Option C (using Apache Spark on EMR) increases management overhead. Option D (splitting jobs) adds complexity and may not handle spikes.
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.
- ✗
Migrate the ETL jobs to Amazon EMR with Apache Spark
Why it's wrong here
EMR adds management overhead and complexity.
- ✓
Use AWS Glue Flex execution to allocate resources dynamically
Why this is correct
Flex execution provides flexible resources that adapt to workload, optimizing cost and performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of DPUs (Data Processing Units) for all jobs
Why it's wrong here
Increases cost during low-volume periods and may not be efficient.
- ✗
Split the jobs into smaller steps and run them sequentially
Why it's wrong here
Does not solve the memory issue and increases complexity.
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: Use AWS Glue Flex execution to allocate resources dynamically — AWS Glue Flex execution allows jobs to use flexible resources that can handle varying memory needs, and it is cost-effective for variable workloads. Option A (increasing DPU) would waste resources during low volume. Option C (using Apache Spark on EMR) increases management overhead. Option D (splitting jobs) adds complexity and may not handle spikes.
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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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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