DEA-C01 Data Ingestion and Transformation Practice Question
A data pipeline uses AWS Glue to read from an Amazon S3 bucket containing millions of small CSV files (each < 1 MB). The ETL job is slow. Which optimization would most improve performance?
⚠ Common exam trap
DEA-C01 often tests the instinct to 'add more workers' for any slow Glue job — candidates miss that small-file overhead is an I/O/metadata problem that horizontal scaling cannot fix.
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 S3 file grouping to combine small files
AWS Glue (and Spark generally) performs poorly with millions of tiny files because each file requires a separate S3 GET request, metadata operation, and task scheduling overhead. S3 file grouping (via 'groupFiles' and 'groupSize' job parameters) coalesces multiple small files into a single read per Spark partition, drastically reducing the number of S3 operations and task overhead. This directly addresses the root cause of the slowness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the ETL script using PySpark instead of Scala
Why it's wrong here
PySpark and Scala both run on the same Spark engine and incur identical per-file overhead when reading millions of small objects. The bottleneck is object count, not language. Rewriting in PySpark suits teams needing Python libraries, not small-file ingestion.
- ✗
Increase the number of Glue workers
Why it's wrong here
Adding workers parallelises listing and reading, but each worker still opens files individually, so per-object request overhead scales with worker count rather than disappearing. Scaling workers suits genuinely large single files or heavy partitions, not millions of sub-1 MB objects.
- ✗
Use the G.1X worker type for more memory
Why it's wrong here
More memory does not address the per-file overhead of opening millions of tiny objects; Glue still issues one request per file, so G.1X workers sit idle between reads. Larger workers suit memory-intensive shuffles or skewed joins, not small-file listing and open costs.
- ✓
Use S3 file grouping to combine small files
Why this is correct
Glue's S3 file grouping (groupFiles) coalesces many small objects into larger partitions per task, cutting per-file listing, opening and request overhead. Millions of sub-1 MB CSVs otherwise dominate runtime, so grouping directly addresses the small-file constraint.
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
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.