The answer is that the groupFiles parameter does not control output file size in AWS Glue because it only affects the input read phase, not the output write phase. When a Glue job writes data to S3, the size of the output Parquet files is determined by the number of Spark partitions during the write operation, not by the groupFiles or groupSize settings. For the AWS Certified Data Engineer Associate DEA-C01 exam, this is a common trap: candidates often assume that groupFiles consolidates output files, but it actually coalesces small input files during the read stage to reduce the number of tasks. In this scenario, the 10 GB input in a single partition is already large, so grouping has no effect—the output files remain small because Spark writes one file per partition by default. A useful memory tip is to think of groupFiles as a "read-only" optimizer: it groups files on the way in, not on the way out.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 data engineer is troubleshooting an AWS Glue job that writes data to an Amazon S3 bucket in Parquet format. The job runs successfully but the output files are smaller than the configured 'groupFiles' size. The engineer has set 'groupFiles' to 'inPartition' and 'groupSize' to 1 GB. The input data is 10 GB in a single partition. What is the most likely reason for the small files?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The 'groupFiles' parameter only affects the input read phase, not the output write phase.
Option B is correct because 'groupFiles' only works when the input data is already small and needs to be coalesced. However, if the input is large and the job writes output, the output file size is determined by the number of Spark partitions, not grouping. The grouping feature only applies to reading input files. Option A is wrong because the setting is correct. Option C is wrong because grouping is a read-time feature, not write-time. Option D is wrong because grouping does not require repartitioning.
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.
✗
The 'groupFiles' parameter is deprecated in the current Glue version.
Why it's wrong here
It is still supported.
✓
The 'groupFiles' parameter only affects the input read phase, not the output write phase.
Why this is correct
Grouping coalesces small input files during reading but does not control output file size.
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.
✗
The 'groupFiles' parameter is misspelled or set incorrectly.
Why it's wrong here
The parameter is correctly set; the issue is that grouping applies to reading, not writing.
✗
The engineer must also set 'repartition' to 1 to merge output files.
Why it's wrong here
Repartitioning is a separate operation; grouping does not require it.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which DEA-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 Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The 'groupFiles' parameter only affects the input read phase, not the output write phase. — Option B is correct because 'groupFiles' only works when the input data is already small and needs to be coalesced. However, if the input is large and the job writes output, the output file size is determined by the number of Spark partitions, not grouping. The grouping feature only applies to reading input files. Option A is wrong because the setting is correct. Option C is wrong because grouping is a read-time feature, not write-time. Option D is wrong because grouping does not require repartitioning.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-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.
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Question Discussion
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