DEA-C01 Data Ingestion and Transformation Practice Question
A company is using AWS Glue ETL to process data from Amazon RDS for MySQL to Amazon S3. The job runs daily and takes 2 hours to complete. The engineer wants to improve performance without increasing cost significantly. Which TWO actions should the engineer take? (Choose TWO.)
⚠ Common exam trap
A common mix-up: candidates confuse 'Auto Scaling' with a performance improvement feature, but Auto Scaling only adjusts resources to match workload, not reduce runtime; the real performance gain comes from reducing data volume via partitioning.
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
✓
Add a partition column to the source table based on a date column.
Adding a partition column (e.g., based on a date column) to the source table enables AWS Glue to use partition pruning during the read phase. This reduces the amount of data scanned and processed by the ETL job, directly improving performance without increasing cost. Partitioning is a common optimization for large datasets in RDS or S3-based sources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a smaller worker type (e.g., G.1X instead of G.2X).
Why it's wrong here
Smaller workers reduce performance.
- ✗
Use Spark DataFrames instead of DynamicFrames.
Why it's wrong here
DynamicFrames are optimized for Glue.
- ✗
Enable 'Auto Scaling' in the Glue job configuration.
Why it's wrong here
Auto Scaling is not available in standard Glue jobs.
- ✓
Add a partition column to the source table based on a date column.
Why this is correct
Partitioning allows Glue to read data in parallel.
- ✓
Increase the number of Glue DPUs.
Why this is correct
More DPUs provide parallel processing and reduce runtime.
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.