Quick Answer
The answer is to increase the number of DPUs, partition the input data, and use columnar formats like Parquet. Partitioning enables parallel processing across workers, while columnar formats like Parquet reduce I/O by reading only the necessary columns, both directly optimizing AWS Glue job performance without increasing cost significantly. Increasing DPUs does raise cost, but it can be cost-effective if the job duration decreases enough to offset the expense, making it a strategic trade-off. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of ETL optimization trade-offs, often appearing as a trap where candidates mistakenly choose to remove partitions or use Spark directly—both of which degrade performance or add unnecessary complexity. A key memory tip: think “Partition, Parquet, Parallelism” to recall the three pillars of Glue optimization.
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 data engineering team uses AWS Glue to run ETL jobs. They notice that jobs are taking longer to complete as data volume grows. They want to optimize performance without increasing cost significantly. Which THREE strategies should they consider?
Remove partitioning from the output
Why wrong: Would worsen performance.
Partition the input data in S3
Enables parallel processing.
Use Amazon EMR instead of Glue
Why wrong: Would increase management overhead and cost.
Convert input data to columnar format (e.g., Parquet)
Reduces data scanned and improves compression.
Increase the number of DPUs (workers)
More parallelism reduces job duration.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Partition the input data in S3
Partitioning the input data allows Glue to process in parallel. Using columnar formats like Parquet reduces I/O. Increasing the number of DPUs (workers) improves parallelism but increases cost; however, it can be cost-effective if job duration decreases significantly. Removing partitions would hurt performance. Using Spark directly is not necessary.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Remove partitioning from the output
Why it's wrong here
Would worsen performance.
Partition the input data in S3
Why this is correct
Enables parallel processing.
Related concept
Read the scenario before looking for a memorised answer.
Use Amazon EMR instead of Glue
Why it's wrong here
Would increase management overhead and cost.
Convert input data to columnar format (e.g., Parquet)
Why this is correct
Reduces data scanned and improves compression.
Related concept
Read the scenario before looking for a memorised answer.
Increase the number of DPUs (workers)
Why this is correct
More parallelism reduces job duration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Partition the input data in S3 — Partitioning the input data allows Glue to process in parallel. Using columnar formats like Parquet reduces I/O. Increasing the number of DPUs (workers) improves parallelism but increases cost; however, it can be cost-effective if job duration decreases significantly. Removing partitions would hurt performance. Using Spark directly is not necessary.
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.
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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