DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer runs an AWS Glue job that writes Parquet files to Amazon S3. The job frequently fails with an error indicating too many small files are being written, causing slow downstream Athena queries. The engineer wants to reduce the number of output files without changing the transformation logic. Which action should the engineer take?
⚠ Common exam trap
The trap here is thinking more DPUs or bookmarks fix small files, when only output partition control (coalesce/repartition) changes file count.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set the Glue job's output to use coalesce or repartition before writing.
Controlling output partitions with coalesce or repartition before writing is the direct way to reduce the number of small Parquet files in an AWS Glue job, improving downstream Athena performance without altering transformation logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the Glue job's output to use coalesce or repartition before writing.
Why this is correct
Using coalesce or repartition in the Spark code before the write step controls the number of output partitions, directly reducing small files. This preserves transformation logic while consolidating output, which is the standard remedy for too many small Parquet files in Glue jobs.
- ✗
Enable bookmarks on the Glue job to track processed files.
Why it's wrong here
Job bookmarks track previously processed data to avoid reprocessing, but they have no effect on how many files are written. Enabling bookmarks would not reduce small files and is unrelated to the output file count issue.
- ✗
Change the output format from Parquet to CSV to reduce file count.
Why it's wrong here
Changing format does not reduce the number of files and would degrade query performance compared to Parquet. CSV is also less efficient for Athena, so this would not solve the small-file problem and would introduce additional downsides.
- ✗
Increase the number of DPUs allocated to the Glue job.
Why it's wrong here
Adding DPUs increases compute capacity but does not directly control the number of output files; in fact, more partitions can produce more files. This does not address the small-file problem and may worsen it, so it is not the right action.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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