DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to perform ETL on data stored in Amazon S3. The Glue job reads CSV files, converts them to Parquet, and partitions by date. The job runs daily and processes about 500 GB of data. The team wants to optimize costs and performance. Which three actions should the team take? (Select THREE.)
⚠ Common exam trap
Many candidates confuse increasing DPUs or shuffle partitions as a universal performance fix, but AWS Glue's cost optimization relies on reducing data processed (column pruning) and choosing appropriate worker types for the workload, not simply scaling resources.
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 column pruning to read only necessary columns in the Glue script.
Column pruning in AWS Glue scripts reduces the amount of data read from Amazon S3 by specifying only the columns needed for the ETL transformation. This minimizes I/O and network overhead, directly lowering costs and improving job performance, especially when processing large CSV files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the Spark shuffle partitions to 500.
Why it's wrong here
More partitions can increase overhead; optimal value depends on data.
- ✓
Use column pruning to read only necessary columns in the Glue script.
Why this is correct
Reduces data scanned and improves performance.
- ✓
Use G.1X or G.2X worker types for better performance.
Why this is correct
These worker types offer more memory for complex transformations.
- ✓
Increase the number of DPUs for the job.
Why this is correct
More DPUs parallelize processing for large datasets.
- ✗
Write the output as JSON instead of Parquet to avoid compression overhead.
Why it's wrong here
JSON is larger and slower to read than Parquet.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.