DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is configuring an AWS Glue ETL job that reads from an Amazon S3 source with many small JSON files and writes to Amazon S3 in Parquet. The job runs slowly and produces many tiny output files. The engineer wants to improve throughput and reduce the number of output files without changing the source data layout. (Choose two.)
⚠ Common exam trap
The trap here is assuming that adding more DPUs will consolidate output files, when output file count is determined by write-time partitions, not by worker capacity.
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
✓
Call coalesce or repartition on the DataFrame before writing the Parquet output.
Small input files cause per-object overhead that slows reads, and the output file count equals the number of write-time partitions. Grouping input files with the groupFiles option consolidates reads, while repartitioning or coalescing the DataFrame before writing reduces the number of output partitions. Together these improve throughput and produce fewer, larger Parquet files without altering the source layout.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Call coalesce or repartition on the DataFrame before writing the Parquet output.
Why this is correct
Repartitioning or coalescing the DataFrame controls how many partitions exist at write time, and each partition produces one output file. Reducing partitions before the write consolidates output into fewer, larger Parquet files, which improves downstream query performance and directly addresses the many-tiny-output-files problem.
- ✓
Enable the AWS Glue groupFiles option to coalesce input files during the read.
Why this is correct
The groupFiles read option lets AWS Glue combine multiple small input objects into a single in-memory partition, which reduces task overhead and improves read throughput for many small JSON files. Since the source layout is unchanged, this addresses the small-file read problem directly and is the intended mechanism for this scenario.
- ✗
Increase the number of Glue DPUs allocated to the job.
Why it's wrong here
Adding DPUs increases parallel capacity but does not reduce the number of output files and can even increase them because more tasks write independently. It may improve speed for large inputs, but with many tiny files the bottleneck is per-file overhead, and output file count is governed by partitioning and repartitioning, not worker count.
- ✗
Enable AWS Glue job bookmarks to skip previously processed files.
Why it's wrong here
Job bookmarks track processed S3 objects across runs to avoid reprocessing, which helps incremental loads but does not affect read throughput for a single run or the number of output files produced. Enabling bookmarks here would not resolve the small-file performance or output fragmentation problems described.
- ✗
Convert the output format from Parquet to CSV to reduce file count.
Why it's wrong here
Switching to CSV does not change how Spark partitions data and writes one file per partition, so the file count remains the same while losing Parquet's columnar compression and predicate pushdown benefits. This degrades analytics performance and does not solve the small-file issue, so it is the wrong direction.
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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