DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue ETL to transform data from an S3 data lake. The job fails with a memory error. Which approach should be used to resolve this issue without major code changes?
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 DPUs allocated to the Glue job
Increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more memory and parallelism. Option A is wrong because rewriting in PySpark is a major code change. Option B is wrong because using a smaller file format may not address memory issues. Option D is wrong because using a different service is unnecessary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Rewrite the ETL script in PySpark instead of Scala
Why it's wrong here
Rewriting the ETL script in PySpark instead of Scala is a major code change, which does not meet the requirement of no major code changes and may not directly fix the memory error.
- ✗
Change the input file format from CSV to Parquet
Why it's wrong here
Changing the input file format from CSV to Parquet can improve compression and performance but does not directly increase memory available to the job, so it is unlikely to resolve a memory error.
- ✓
Increase the number of DPUs allocated to the Glue job
Why this is correct
Increasing the number of DPUs allocated to the Glue job directly increases memory and parallelism, which helps resolve memory errors without major code changes.
- ✗
Use Amazon EMR instead of AWS Glue
Why it's wrong here
Using Amazon EMR instead of AWS Glue is a significant architectural change and does not satisfy the requirement of no major code changes.
Visual reference
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.