The answer is to use AWS Glue ETL to consolidate small files into larger ones before crawling. This is correct because a high volume of small files forces the Glue crawler to perform an excessive number of S3 list and sample operations, which increases metadata overhead and can cause the crawler to exceed its 24-hour timeout limit. By running a compaction job—using options like groupFiles or groupSize in an ETL script—you reduce the total object count, directly improving Glue crawler performance when dealing with small files. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how S3 object count impacts Glue’s metadata operations, often appearing as a trap where candidates mistakenly choose to increase crawler frequency or adjust partition structure instead. A common memory tip: think of the crawler as a librarian—it’s faster to catalog a few thick books than thousands of sticky notes. Remember: fewer files, faster crawls.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 engineering team is using AWS Glue to catalog data in an S3 data lake. They have a Glue crawler that runs daily to update the Data Catalog. Recently, they noticed that the crawler is taking longer to run and sometimes fails because of a timeout. The team suspects the issue is due to the large number of small files in the S3 bucket. They need to improve crawler performance and reliability. Which solution should they implement?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue ETL to consolidate small files into larger ones before crawling.
Option B is correct because consolidating small files into larger ones (e.g., using AWS Glue ETL with a groupFiles or groupSize option, or a separate compaction job) reduces the number of objects the crawler must list and sample. This directly addresses the root cause: a high volume of small files increases metadata operations and can cause crawler timeouts. By reducing file count, the crawler can complete within the default 24-hour timeout and avoid failures.
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.
✗
Configure the crawler to use a different classifier.
Why it's wrong here
Classifier doesn't fix file count issue.
✓
Use AWS Glue ETL to consolidate small files into larger ones before crawling.
Why this is correct
Reduces number of files to scan.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the crawler timeout to 24 hours.
Why it's wrong here
Does not address small file issue.
✗
Schedule the crawler to run more frequently to avoid large data accumulation.
Why it's wrong here
Would increase load and potentially cause more timeouts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume increasing the timeout or running the crawler more frequently will fix performance issues, but the real bottleneck is the sheer number of small files, which requires data compaction to resolve.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue crawlers use S3 ListObject API calls to discover partitions and files; a large number of small files (e.g., < 128 MB) increases the number of API requests and can hit S3 request rate limits. Consolidating files into larger sizes (ideally 128 MB or more) reduces the number of objects, improves partition pruning, and lowers the crawler's memory footprint. In real-world scenarios, teams often use a scheduled Glue ETL job with a 'groupFiles' option or a Spark coalesce/repartition to compact data before the daily crawl.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue ETL to consolidate small files into larger ones before crawling. — Option B is correct because consolidating small files into larger ones (e.g., using AWS Glue ETL with a groupFiles or groupSize option, or a separate compaction job) reduces the number of objects the crawler must list and sample. This directly addresses the root cause: a high volume of small files increases metadata operations and can cause crawler timeouts. By reducing file count, the crawler can complete within the default 24-hour timeout and avoid failures.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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