- A
Increase the number of DPUs allocated to the Glue job
More DPUs provide more memory and compute resources.
- B
Use S3 Select to filter data before reading into the Glue job
Why wrong: S3 Select pushes down filtering but does not directly resolve out-of-memory errors.
- C
Use Spark's 'coalesce' function to reduce the number of partitions
Why wrong: Coalesce may not free memory; it can even cause out-of-memory if data skew exists.
- D
Optimize the transformation logic to use less memory, for example by filtering early
Reducing data volume early reduces memory pressure.
- E
Use a larger worker type, such as G.2X
Larger worker types have more memory per worker.
Question 1,398 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is using AWS Glue to run ETL jobs that transform data from S3 to Redshift. The jobs are failing intermittently with out-of-memory errors. Which THREE actions can help resolve this issue? (Choose THREE.)
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 allocated to the Glue job provides more memory and compute resources for the Spark executors, directly addressing out-of-memory errors by allowing larger datasets to be processed without exceeding heap limits. This is a standard scaling approach for memory-intensive ETL workloads in AWS Glue.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 DPUs allocated to the Glue job
Why this is correct
More DPUs provide more memory and compute resources.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use S3 Select to filter data before reading into the Glue job
Why it's wrong here
S3 Select pushes down filtering but does not directly resolve out-of-memory errors.
- ✗
Use Spark's 'coalesce' function to reduce the number of partitions
Why it's wrong here
Coalesce may not free memory; it can even cause out-of-memory if data skew exists.
- ✓
Optimize the transformation logic to use less memory, for example by filtering early
Why this is correct
Reducing data volume early reduces memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a larger worker type, such as G.2X
Why this is correct
Larger worker types have more memory per worker.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse reducing data volume (S3 Select) with increasing memory capacity, or mistakenly believe coalescing partitions always reduces memory usage, when in fact it can concentrate data and exacerbate OOM errors.
Detailed technical explanation
How to think about this question
AWS Glue jobs run on Apache Spark, where each executor has a fixed memory heap defined by the worker type (e.g., G.1X has 16 GB, G.2X has 32 GB). Out-of-memory errors typically occur when a single partition's data exceeds the executor's available memory for shuffle or aggregation operations. Increasing DPUs adds more executors (horizontal scaling), while using a larger worker type (e.g., G.2X) increases per-executor memory (vertical scaling), both of which can mitigate OOM errors depending on the bottleneck.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of DPUs allocated to the Glue job — Increasing the number of DPUs allocated to the Glue job provides more memory and compute resources for the Spark executors, directly addressing out-of-memory errors by allowing larger datasets to be processed without exceeding heap limits. This is a standard scaling approach for memory-intensive ETL workloads in AWS Glue.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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