DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is ingesting data from multiple sources into Amazon S3 using AWS Glue ETL jobs. The jobs are failing intermittently with the error: 'Task ran out of memory'. The input data size varies widely from 100 MB to 10 GB per job. Which configuration change would best mitigate this issue?
⚠ Common exam trap
Test-takers frequently assume increasing parallelism (Option A) is the universal fix for memory errors, but the root cause is the variable data volume per run, which job bookmarking mitigates by ensuring each run processes only a manageable subset of data.
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
✓
Enable job bookmarking to process only incremental data
Enabling job bookmarking allows the Glue ETL job to process only incremental (new or changed) data, which directly addresses the intermittent out-of-memory errors caused by widely varying input sizes (100 MB to 10 GB). By skipping previously processed data, the job consistently handles smaller data volumes per run, reducing memory pressure on the Spark executors.
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 number of workers in the Glue job
Why it's wrong here
More workers increase parallelism but each worker still has memory limits; the job may still fail if the data is skewed.
- ✓
Enable job bookmarking to process only incremental data
Why this is correct
Bookmarking reduces the data processed each run, lowering memory requirements.
- ✗
Reduce the batch size in the S3 source node
Why it's wrong here
Reducing batch size may help but not as effectively as bookmarking for varying data sizes.
- ✗
Change the job type from Spark to Python shell
Why it's wrong here
Python shell is not suitable for large data transformations.
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.