The answer is to switch the worker type to G.2X while keeping 10 workers. This resolves the AWS Glue out of memory error because G.2X workers double the memory per executor from 16 GB to 32 GB compared to G.1X, directly addressing the Spark executor memory exhaustion that caused the job to fail when processing several terabytes of CSV files. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of how worker type selection impacts memory allocation in Glue ETL jobs, with a common trap being the instinct to add more G.1X workers—which spreads data but doesn't fix per-executor memory limits—or to use expensive G.4X workers unnecessarily. The key insight is that for memory-bound jobs, upgrading worker type is often more cost-effective than scaling out worker count. Memory tip: think "G.2X gives double the memory per box, not more boxes."
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.
Exhibit
Refer to the exhibit.
Error log from AWS Glue job:
```
An error occurred while calling o123.pyWriteDynamicFrame.
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 (TID 8, ip-10-0-1-45.ec2.internal): java.lang.OutOfMemoryError: Java heap space
```
A data engineer runs an AWS Glue ETL job that reads from a large Amazon S3 source (several terabytes of CSV files) and writes transformed data to an S3 bucket in Parquet format. The job fails with the error shown in the exhibit. The job uses the Standard worker type with 10 workers (G.1X). The engineer needs to resolve the failure with minimal cost increase. What should the engineer do?
Refer to the exhibit.
Error log from AWS Glue job:
```
An error occurred while calling o123.pyWriteDynamicFrame.
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 4 times, most recent failure: Lost task 0.3 in stage 4.0 (TID 8, ip-10-0-1-45.ec2.internal): java.lang.OutOfMemoryError: Java heap space
```
A
Increase the number of workers to 20 while keeping G.1X worker type.
Why wrong: More workers spread the workload but each executor still has limited memory; the OOM may still occur.
B
Change the worker type to G.2X with 10 workers.
G.2X provides double the memory (32 GB) per worker compared to G.1X (16 GB), resolving the heap space error with minimal cost increase.
C
Change the worker type to G.4X with 10 workers.
Why wrong: G.4X provides 4 vCPUs and 16 GB memory (same as G.1X); it does not increase memory per executor.
D
Set the 'coalesce' parameter to reduce the number of output files.
Why wrong: Coalesce reduces partitions but does not increase executor memory; it might even cause more OOM if partitions are combined.
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 with 10 workers.
Option D is correct because the OutOfMemoryError indicates that the Spark executors do not have enough memory; switching to G.2X workers doubles memory per worker (from 16 GB to 32 GB) without increasing the number of workers, which is more cost-effective than increasing the number of workers with G.1X. Option A is wrong because increasing the number of workers with G.1X may not resolve the memory issue per executor; it spreads the data across more executors but each still has limited memory. Option B is wrong because reducing the number of files (coalesce) may not help if the issue is per-task memory. Option C is wrong because using the G.2X worker type with more memory per worker is likely sufficient; G.4X may be unnecessary and more expensive.
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 workers to 20 while keeping G.1X worker type.
Why it's wrong here
More workers spread the workload but each executor still has limited memory; the OOM may still occur.
✓
Change the worker type to G.2X with 10 workers.
Why this is correct
G.2X provides double the memory (32 GB) per worker compared to G.1X (16 GB), resolving the heap space error with minimal cost increase.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the worker type to G.4X with 10 workers.
Why it's wrong here
G.4X provides 4 vCPUs and 16 GB memory (same as G.1X); it does not increase memory per executor.
✗
Set the 'coalesce' parameter to reduce the number of output files.
Why it's wrong here
Coalesce reduces partitions but does not increase executor memory; it might even cause more OOM if partitions are combined.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Change the worker type to G.2X with 10 workers. — Option D is correct because the OutOfMemoryError indicates that the Spark executors do not have enough memory; switching to G.2X workers doubles memory per worker (from 16 GB to 32 GB) without increasing the number of workers, which is more cost-effective than increasing the number of workers with G.1X. Option A is wrong because increasing the number of workers with G.1X may not resolve the memory issue per executor; it spreads the data across more executors but each still has limited memory. Option B is wrong because reducing the number of files (coalesce) may not help if the issue is per-task memory. Option C is wrong because using the G.2X worker type with more memory per worker is likely sufficient; G.4X may be unnecessary and more expensive.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data pipeline uses AWS Glue to process large CSV files. The team notices that some jobs fail with out-of-memory errors. Which TWO configuration changes can help mitigate this issue?
hard
A.Reduce the number of DPUs to limit concurrency.
✓ B.Increase the number of DPUs for the Glue job.
✓ C.Enable Glue job autoscaling.
D.Convert input files from CSV to Parquet.
E.Enable job bookmarks.
Why B: Options A and D are correct: increasing DPUs and enabling autoscaling provide more memory. Option B (reducing DPUs) would worsen the problem. Option C (conversion to Parquet) may reduce memory but is not a direct configuration change for the Glue job. Option E (job bookmarks) does not affect memory.
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Question Discussion
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