DEA-C01 Data Ingestion and Transformation Practice Question
A retail company uses AWS Glue to process daily sales data from multiple CSV files stored in Amazon S3. The Glue job runs a PySpark script that reads the files, performs joins, and writes the output as Parquet. Recently, the job has been failing with 'Out of Memory' errors. The data volume has grown from 10 GB to 50 GB per day. The Glue job uses 10 DPUs and the standard worker type. The data engineer needs to fix the job without rewriting the script. What should the data engineer do?
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
✓
Change the worker type to G.2X to get more memory per worker.
Out of Memory errors in AWS Glue are typically caused by insufficient per-executor memory during operations like joins. Changing the worker type to G.2X doubles the memory per DPU (from 16 GB to 32 GB), directly addressing the OOM issue without rewriting the script. Option D is wrong because increasing the number of DPUs adds more executors but does not increase the memory per executor; it only increases parallelism, which may not resolve OOM if a single executor runs out of memory. Option A (splitting input files) does not reduce the memory footprint of joins. Option C (decreasing DPUs) reduces resources and worsens the problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Split the input CSV files into smaller partitions.
Why it's wrong here
Splitting input CSV files into smaller partitions does not reduce the memory required for joins; it only changes input parallelism.
- ✓
Change the worker type to G.2X to get more memory per worker.
Why this is correct
Changing to G.2X worker type doubles the memory per DPU, which directly addresses Out of Memory errors by providing more per-executor memory for operations like joins.
- ✗
Decrease the number of DPUs to reduce memory contention.
Why it's wrong here
Decreasing the number of DPUs reduces total memory and compute capacity, worsening the Out of Memory issue.
- ✗
Increase the number of DPUs for the Glue job to 20.
Why it's wrong here
Increasing the number of DPUs adds more executors but does not increase per-executor memory; the OOM error arises from insufficient memory in a single executor, so this change is ineffective.
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.