The answer is to create a separate Glue job that runs before the main job to consolidate small files into larger ones in the source bucket. This directly addresses the root cause of poor performance when optimizing AWS Glue performance with many small files, as the overhead of opening, reading, and closing millions of 100 KB files overwhelms the Spark engine, even with the 'Group Files' option enabled. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that file size distribution is a critical factor in ETL performance, often more impactful than compute resources like DPUs. A common trap is to assume that simply increasing parallelism or switching to EMR will solve the issue, but the fundamental bottleneck remains the metadata and I/O overhead from tiny files. Remember the memory tip: "Small files, big problems—compact before you process."
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 runs a data pipeline using AWS Glue ETL jobs that process about 10 TB of data daily from Amazon S3. The jobs are triggered by a schedule and write results to a separate S3 bucket. Recently, the jobs have been taking longer to complete, and the data engineering team has observed that the number of files in the source bucket has increased significantly, from thousands to millions of small files (each about 100 KB). The Glue jobs are configured to use the 'Group Files' option, but performance is still poor. The team needs to improve the job performance without changing the source data generation process. Which course of action should the team take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a separate Glue job that runs before the main job to consolidate small files into larger ones in the source bucket
Using an AWS Glue job to periodically compact small files into larger files (e.g., 100 MB) before the main ETL job runs will reduce the overhead of reading millions of small files. Option B is wrong because increasing DPUs may help but does not address the root cause of many small files. Option C is wrong because using Spark on EMR may still suffer from small file issue. Option D is wrong because Lambda has limitations on processing large numbers of files.
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 allocated to the existing Glue job
Why it's wrong here
More DPUs provide parallelism but each file still requires overhead.
✗
Switch the ETL processing to Amazon EMR with Spark
Why it's wrong here
Spark also suffers from small file overhead if not addressed.
✗
Use AWS Lambda to pre-process the files and combine them
Why it's wrong here
Lambda has a 15-minute timeout and is not ideal for large-scale consolidation.
✓
Create a separate Glue job that runs before the main job to consolidate small files into larger ones in the source bucket
Why this is correct
Consolidation reduces the number of files, improving read performance.
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 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 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: Create a separate Glue job that runs before the main job to consolidate small files into larger ones in the source bucket — Using an AWS Glue job to periodically compact small files into larger files (e.g., 100 MB) before the main ETL job runs will reduce the overhead of reading millions of small files. Option B is wrong because increasing DPUs may help but does not address the root cause of many small files. Option C is wrong because using Spark on EMR may still suffer from small file issue. Option D is wrong because Lambda has limitations on processing large numbers of files.
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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Question Discussion
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