DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue ETL to transform a large dataset in S3. The job processes 2 TB of data daily and currently runs for 6 hours. The engineer wants to reduce runtime without changing the transformation logic. What is the best approach?
⚠ Common exam trap
It's easy for candidates to think reducing DPUs reduces overhead and speeds up the job, but in distributed systems, more parallelism (more DPUs) reduces runtime for large datasets, while reducing DPUs increases it.
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
✓
Increase the number of Glue DPUs or enable auto-scaling.
Increasing the number of DPUs or enabling auto-scaling directly allocates more distributed processing capacity to the AWS Glue job, which reduces runtime for large datasets by parallelizing the workload across more resources. Since the transformation logic is fixed and the job is already running on Spark, adding compute capacity is the most straightforward way to speed up processing without code changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of DPUs to minimize overhead.
Why it's wrong here
Fewer DPUs will increase runtime.
- ✗
Use the Spark UI to analyze bottlenecks and rewrite code.
Why it's wrong here
The question states no change to transformation logic.
- ✓
Increase the number of Glue DPUs or enable auto-scaling.
Why this is correct
More DPUs provide parallel processing and reduce runtime.
- ✗
Switch from Spark to Python shell.
Why it's wrong here
Python shell is single-node and slower for large data.
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 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.