- A
Increase the number of DPUs for the Glue job.
Why wrong: More DPUs add cost; may not resolve memory issue if the problem is per-worker memory.
- B
Change the output format from Parquet to CSV.
Why wrong: CSV does not reduce memory; may increase I/O.
- C
Use the JDBC connection with fetchSize parameter.
Why wrong: fetchSize affects reading, not writing.
- D
Configure the write operation with 'groupSize' to limit records per file.
Limiting records per file reduces the memory needed for buffering during writes.
Quick Answer
The correct answer is to configure the write operation with 'groupSize' to limit records per file. This resolves AWS Glue memory errors during write with groupSize by capping the number of records written to each Parquet file, which directly reduces the memory footprint per task during the write phase. When processing a wide schema of 200 columns across 500 GB of data, the default writer can overload the executor’s memory by attempting to buffer too many rows at once; groupSize forces smaller, more manageable batches. On the MLS-C01 exam, this scenario tests your understanding of Glue’s memory management and file partitioning strategies—a common trap is to immediately scale DPUs, but that only masks the issue with higher cost. Remember the mnemonic: “Group to unload, not scale to fail”—groupSize controls memory pressure, while scaling DPUs just adds more fuel to the fire.
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 has an AWS Glue ETL job that reads data from an Amazon RDS for MySQL table and writes to Amazon S3 in Parquet format. The job runs daily and processes 500 GB of data. Recently, the job has been failing with memory errors during the write phase. The data schema is wide (200 columns). Which change should a data engineer make to the Glue job to resolve the memory issue?
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
Configure the write operation with 'groupSize' to limit records per file.
Option B is correct. Using the 'groupSize' or 'maxRecordsPerFile' option in Glue's DynamicFrame writer can control the number of records per Parquet file, reducing memory pressure. Option A is wrong because increasing DPUs may help but is a costlier solution. Option C is wrong because JDBC connection is for reading, not writing. Option D is wrong because using CSV is less efficient and doesn't address memory.
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 number of DPUs for the Glue job.
Why it's wrong here
More DPUs add cost; may not resolve memory issue if the problem is per-worker memory.
- ✗
Change the output format from Parquet to CSV.
Why it's wrong here
CSV does not reduce memory; may increase I/O.
- ✗
Use the JDBC connection with fetchSize parameter.
Why it's wrong here
fetchSize affects reading, not writing.
- ✓
Configure the write operation with 'groupSize' to limit records per file.
Why this is correct
Limiting records per file reduces the memory needed for buffering during writes.
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.
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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: Configure the write operation with 'groupSize' to limit records per file. — Option B is correct. Using the 'groupSize' or 'maxRecordsPerFile' option in Glue's DynamicFrame writer can control the number of records per Parquet file, reducing memory pressure. Option A is wrong because increasing DPUs may help but is a costlier solution. Option C is wrong because JDBC connection is for reading, not writing. Option D is wrong because using CSV is less efficient and doesn't address memory.
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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