DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL jobs to transform data from Amazon S3 to Amazon Redshift. The job reads JSON files, applies schema mapping, and writes to a Redshift table. Recently, the job started failing with memory errors. The data volume has increased tenfold. Which approach should a data engineer take to resolve this issue with minimal code changes?
⚠ Common exam trap
A common mix-up: candidates assume memory errors always require code optimization (e.g., batching or partitioning), but the question explicitly asks for minimal code changes, making resource scaling the correct answer.
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 AWS Glue job directly addresses the memory constraint caused by a tenfold increase in data volume. Glue ETL jobs run on Apache Spark, which distributes data processing across executors; more DPUs provide more memory and compute capacity, allowing the job to handle larger datasets 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.
- ✗
Switch from Spark to Python Shell job type.
Why it's wrong here
Python Shell has limited memory.
- ✗
Implement batch processing with smaller file sizes.
Why it's wrong here
Requires code changes and may not be sufficient.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
Provides more resources for processing.
- ✗
Use Redshift Spectrum to query data directly from S3.
Why it's wrong here
Does not help Glue job memory.
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
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