DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue Studio to build a visual ETL job that reads JSON files from Amazon S3, applies a mapping transform, and writes Parquet to another S3 location. The engineer notices that the job is writing many small files, which hurts downstream query performance. Which action should the engineer take to reduce the number of output files without changing the source data?
⚠ Common exam trap
The trap here is assuming that more compute resources will consolidate output, when adding workers usually increases the number of output files.
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
✓
Use the repartition or coalesce transform before the S3 target to reduce the number of output partitions.
The number of files written by a Glue job corresponds to the number of partitions in the DataFrame at write time. Repartition or coalesce reduces that partition count, so the job writes fewer, larger files. This directly addresses the small-file issue without altering the source data or the output format.
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 the repartition or coalesce transform before the S3 target to reduce the number of output partitions.
Why this is correct
Repartition or coalesce changes the number of partitions in the DataFrame, which directly controls how many files are written. Coalesce is especially useful when reducing partitions without a full shuffle. Applying it before the target write consolidates output into fewer, larger files and improves downstream query performance.
- ✗
Increase the number of DPUs for the Glue job so more workers write files in parallel.
Why it's wrong here
Increasing DPUs adds more workers, which typically produces more output partitions and therefore more small files, not fewer. More parallelism helps throughput but does not consolidate the output, so it works against the goal of reducing file count in this scenario.
- ✗
Enable the Glue job bookmark so the job only processes new files on each run.
Why it's wrong here
Job bookmarks track which source data has already been processed to avoid reprocessing. They do not change how many files the job writes to the target location, so enabling bookmarks has no effect on the small-file problem described here.
- ✗
Change the output format from Parquet to CSV so the files are smaller and easier to manage.
Why it's wrong here
Switching to CSV does not reduce the number of files and actually makes each file less efficient for analytics because CSV is row-based and uncompressed by default. The small-file problem persists, and query performance typically gets worse rather than better.
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 |
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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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