The correct solution is to use AWS Glue ETL to read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files. This directly addresses the root cause of poor Athena query performance and high costs: thousands of tiny CSV files force Athena to perform excessive S3 LIST and GET operations, while the row-based CSV format forces full table scans. Converting to Parquet, a columnar format, dramatically reduces the amount of data scanned per query, and consolidating the files into larger blocks (ideally 128 MB or more) minimizes metadata overhead and improves throughput. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how file size, format, and partitioning impact Athena’s cost model—a common trap is choosing to simply compress the existing CSV files, which still leaves the small-file problem unsolved. Remember the memory tip: “Small files, big bills; Parquet and Glue give you thrills.”
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 Amazon S3 to store historical stock market data as CSV files. They run daily Amazon Athena queries to generate reports. Recently, the finance team reported that queries are timing out and costs have increased significantly. The data engineering team notices that the S3 bucket contains thousands of small files (average 100 KB) due to a misconfigured ingestion pipeline. They need to improve query performance and reduce costs without changing the existing reporting schedule. The team has access to AWS Glue and can create new tables. 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 read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files.
Option D is correct because converting the thousands of small CSV files into fewer, larger Parquet files using AWS Glue ETL directly addresses the root cause of poor Athena performance and high costs. Parquet is a columnar format that reduces the amount of data scanned per query, and larger files minimize the overhead of S3 LIST and GET operations, improving throughput. This solution does not change the reporting schedule and leverages existing Glue capabilities to create new optimized tables.
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.
✗
Partition the data by date and create a new Athena table with partitions.
Why it's wrong here
Partitioning helps but does not consolidate small files.
✗
Use S3 Select to filter rows within each file before Athena processes them.
Why it's wrong here
S3 Select is not integrated with Athena in this way.
✗
Increase the Athena query timeout to 30 minutes.
Why it's wrong here
Does not address root cause; still scans many small files.
✓
Use AWS Glue ETL to read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files.
Why this is correct
Consolidates small files and uses columnar format to reduce scan size.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume partitioning (Option A) is a universal performance fix, but they overlook that partitioning does not address the 'small files problem' which is a distinct performance killer in Athena due to S3 request overhead and file open costs.
Detailed technical explanation
How to think about this question
Athena charges based on the amount of data scanned per query, and Parquet files with compression (e.g., Snappy) can reduce scanned data by up to 80% compared to CSV. Additionally, Glue ETL can coalesce the small files into files of optimal size (e.g., 128 MB or larger) to match the HDFS block size, reducing the number of S3 GET requests and improving parallelism. The conversion also enables predicate pushdown and column pruning in Athena, which further reduces I/O.
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 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 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 read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files. — Option D is correct because converting the thousands of small CSV files into fewer, larger Parquet files using AWS Glue ETL directly addresses the root cause of poor Athena performance and high costs. Parquet is a columnar format that reduces the amount of data scanned per query, and larger files minimize the overhead of S3 LIST and GET operations, improving throughput. This solution does not change the reporting schedule and leverages existing Glue capabilities to create new optimized tables.
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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