The answer is concatenating the small files into larger ones because the root cause is S3 LIST overhead from millions of tiny CSV files. AWS Glue DataBrew samples data by listing and reading files from S3, and when you have many small files, each under 1 MB, the sheer number of S3 LIST and GET requests creates a bottleneck that dramatically slows down sample loading. This question tests your understanding of how S3 request costs and latency scale with object count, a common performance pitfall on the AWS Certified Data Engineer Associate DEA-C01 exam. A frequent trap is assuming more DPUs will fix the slowness, but compute power does not reduce the overhead of listing millions of objects—only reducing the file count does. Remember the memory tip: "Small files, big pain; combine them to gain."
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 uses AWS Glue DataBrew for data preparation. The data source is an S3 bucket with millions of small CSV files (each < 1 MB). The DataBrew project takes a long time to load the sample data. What is the most likely cause and solution?
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 large number of small files causes S3 LIST overhead; concatenate files into larger files
Option B is correct because DataBrew samples data by reading files, and many small files cause high overhead due to S3 LIST and GET requests. Concatenating files into fewer larger files reduces this overhead. Option A (increase DPUs) does not help with the LIST overhead. Option C (use Glue ETL) is a different service. Option D (use Athena) is for querying, not data preparation.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Amazon Athena to query the data instead of DataBrew
Why it's wrong here
Athena also performs poorly with many small files due to high overhead.
✗
The DataBrew job is under-provisioned; increase the number of DPUs
Why it's wrong here
DPUs help with processing, but the bottleneck is reading many small files from S3.
✓
The large number of small files causes S3 LIST overhead; concatenate files into larger files
Why this is correct
S3 performance degrades with many small files; combining them reduces API calls.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use AWS Glue ETL instead of DataBrew for this volume
Why it's wrong here
Glue ETL also faces similar S3 overhead issues with many small files.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Similar concept trap
Glue ETL also faces similar S3 overhead issues with many small files.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: The large number of small files causes S3 LIST overhead; concatenate files into larger files — Option B is correct because DataBrew samples data by reading files, and many small files cause high overhead due to S3 LIST and GET requests. Concatenating files into fewer larger files reduces this overhead. Option A (increase DPUs) does not help with the LIST overhead. Option C (use Glue ETL) is a different service. Option D (use Athena) is for querying, not data preparation.
What should I do if I get this DEA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
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?
Static NAT maps one inside address to one outside address.
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