DEA-C01 Data Operations and Support Practice Question
A data engineer is troubleshooting an AWS Glue job that fails with 'java.lang.OutOfMemoryError: Java heap space'. The job processes a large dataset. Which TWO configuration changes should the engineer consider to resolve this issue? (Choose TWO.)
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 shuffle partitions configuration (spark.sql.shuffle.partitions).
Options B and D are correct. Increasing Spark shuffle partitions (B) reduces the amount of data shuffled per partition, lowering memory pressure and preventing heap overflow. Increasing the number of DPUs (D) allocates more memory and compute resources to the Glue job, directly addressing heap space limitations. Option A is incorrect because changing the output format from Parquet to CSV does not reduce memory usage and may increase it due to lack of compression. Option C is incorrect because reducing the number of source partitions can increase partition size, worsening memory issues. Option E is incorrect because disabling job bookmarks does not affect memory usage; it may cause processing of already processed data but doesn't resolve heap space.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the output format from Parquet to CSV.
Why it's wrong here
Data format does not affect Spark shuffle memory.
- ✓
Increase the Spark shuffle partitions configuration (spark.sql.shuffle.partitions).
Why this is correct
More partitions reduce data per partition, lowering memory usage.
- ✗
Reduce the number of partitions in the source data.
Why it's wrong here
Reducing partitions increases partition size, worsening memory issues.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
More DPUs provide more memory and compute resources.
- ✗
Disable job bookmarks to avoid incremental processing.
Why it's wrong here
Job bookmarks do not cause memory errors.
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Same concept, more angles
1 more way this is tested on DEA-C01
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 engineer is troubleshooting an AWS Glue ETL job that fails with a memory error when processing a large dataset. Which approach can help reduce memory usage?
easy- A.Set the job to use only one worker
- B.Reduce the number of partitions in the data source
- ✓ C.Increase the number of workers for the job
- D.Increase the worker type to G.2X
Why C: Increasing the number of workers distributes the workload across more resources, reducing memory pressure per worker. Option A is incorrect because using only one worker reduces parallelism and increases memory consumption per worker, worsening the issue. Option B is incorrect because reducing partitions increases the size of each partition, leading to higher memory usage per task. Option D is incorrect because although increasing the worker type to G.2X provides more memory per worker, it does not increase parallelism and may be less cost-effective than increasing the number of workers.
JA
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.