- A
Increase the Spark memory overhead parameter in the Glue job configuration.
Allocates more memory per worker for Spark processing, reducing OOM errors.
- B
Use DynamicFrame instead of Spark DataFrame for transformations.
Why wrong: DynamicFrame is built on Spark; memory usage is similar.
- C
Increase the number of workers to maximum allowed.
Why wrong: More workers increase parallelism and cost but each worker still has limited memory; may not solve OOM if memory per worker is insufficient.
- D
Switch from a Spark job to a Python shell job.
Why wrong: Python shell jobs are for lightweight processing; not suitable for large transformations.
- E
Change the worker type from 'G.1x' to 'G.2x' to double memory per worker.
Doubles memory per worker, addressing OOM with moderate cost increase.
Cost-Effective AWS Glue Out-of-Memory Fix
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 Amazon S3 to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. The team wants to resolve this issue without increasing costs significantly. Which TWO actions should the team take?
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 Spark memory overhead parameter in the Glue job configuration.
Options A and E are both correct. Option A increases the Spark memory overhead parameter (`spark.executor.memoryOverhead`), which allocates additional off-heap memory for Spark operations. This is a cost-effective tuning approach that can prevent 'Out of Memory' errors without adding more workers or changing job types. Option E changes the worker type from 'G.1x' to 'G.2x', doubling the memory per worker. While this increases per-worker cost, it avoids adding more workers (which would increase cost significantly) and is a targeted fix for memory-intensive workloads. Both options address memory pressure without the high cost of increasing the number of workers (Option C) or the performance loss of switching to Python shell (Option D).
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 Spark memory overhead parameter in the Glue job configuration.
Why this is correct
Allocates more memory per worker for Spark processing, reducing OOM errors.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use DynamicFrame instead of Spark DataFrame for transformations.
Why it's wrong here
DynamicFrame is built on Spark; memory usage is similar.
- ✗
Increase the number of workers to maximum allowed.
Why it's wrong here
More workers increase parallelism and cost but each worker still has limited memory; may not solve OOM if memory per worker is insufficient.
- ✗
Switch from a Spark job to a Python shell job.
Why it's wrong here
Python shell jobs are for lightweight processing; not suitable for large transformations.
- ✓
Change the worker type from 'G.1x' to 'G.2x' to double memory per worker.
Why this is correct
Doubles memory per worker, addressing OOM with moderate cost increase.
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 may assume increasing the number of workers (Option C) is the only way to fix OOM errors, but this ignores the cost-effective tuning of memory overhead and worker type upgrades.
Trap categories for this question
Similar concept trap
DynamicFrame is built on Spark; memory usage is similar.
Detailed technical explanation
How to think about this question
Under the hood, Spark executors allocate memory for execution, storage, and overhead. The `spark.executor.memoryOverhead` parameter (default 10% of executor memory) covers off-heap memory for things like JVM internals, thread stacks, and network buffers. In Glue, when transformations involve large shuffles or complex operations, the default overhead may be insufficient, causing OOM errors. Increasing this parameter (e.g., to 20-30%) provides a safety margin without requiring more expensive worker types.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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
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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 Spark memory overhead parameter in the Glue job configuration. — Options A and E are both correct. Option A increases the Spark memory overhead parameter (`spark.executor.memoryOverhead`), which allocates additional off-heap memory for Spark operations. This is a cost-effective tuning approach that can prevent 'Out of Memory' errors without adding more workers or changing job types. Option E changes the worker type from 'G.1x' to 'G.2x', doubling the memory per worker. While this increases per-worker cost, it avoids adding more workers (which would increase cost significantly) and is a targeted fix for memory-intensive workloads. Both options address memory pressure without the high cost of increasing the number of workers (Option C) or the performance loss of switching to Python shell (Option D).
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
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