The answer is to use S3 object grouping or batch operations to combine small files before Glue processing. This optimization directly addresses the core problem: AWS Glue small file optimization is critical because Spark’s overhead per file—scheduling tasks, opening S3 connections, and reading metadata—scales with file count, not data volume. By grouping millions of tiny 50–200 KB JSON files into larger objects (e.g., 100–500 MB), you drastically reduce the number of Spark tasks, cutting both job runtime and cost without increasing DPUs or worker types. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of Spark’s file-level parallelism and S3’s request costs; a common trap is assuming more compute resources (DPUs or larger workers) will fix a small-file bottleneck, but that only raises cost without solving the root issue. Remember: Parquet is already optimal for columnar storage, so the fix is file size, not format. Memory tip: “Bigger files, fewer tasks—small files burn cash.”
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 data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use S3 object grouping or batch operations to combine small files before Glue processing.
Option C is correct. Using S3 object grouping (e.g., S3 batch operations or partitioning) to create larger files reduces the number of tasks and overhead in Spark. Option A is wrong because increasing DPUs increases cost without addressing the small file problem. Option B is wrong because converting to CSV is not more efficient than Parquet; Parquet is columnar and efficient. Option D is wrong because increasing worker type also increases cost without solving small file 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.
✗
Increase the worker type to G.2X for more memory per worker.
Why it's wrong here
Larger workers increase cost; does not fix the small file issue.
✗
Increase the number of DPUs allocated to the Glue job.
Why it's wrong here
More DPUs increase parallelism but also cost; does not address small file overhead.
✗
Change the output format from Parquet to CSV to reduce compression overhead.
Why it's wrong here
CSV is not columnar and may increase I/O; Parquet is optimized for analytics.
✓
Use S3 object grouping or batch operations to combine small files before Glue processing.
Why this is correct
Combining small files reduces task overhead, leading to faster and cheaper jobs.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
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.
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 S3 object grouping or batch operations to combine small files before Glue processing. — Option C is correct. Using S3 object grouping (e.g., S3 batch operations or partitioning) to create larger files reduces the number of tasks and overhead in Spark. Option A is wrong because increasing DPUs increases cost without addressing the small file problem. Option B is wrong because converting to CSV is not more efficient than Parquet; Parquet is columnar and efficient. Option D is wrong because increasing worker type also increases cost without solving small file 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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