The answer is to increase the number of DPUs allocated to the Glue job. This resolves the java.lang.OutOfMemoryError: Java heap space because each DPU provides 16 GB of memory and 4 vCPUs, so adding more DPUs linearly scales the total memory available across Spark executors, preventing heap exhaustion when processing larger CSV files. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Glue’s resource scaling and the common trap of confusing DPUs with worker types—remember that increasing DPUs adds memory, while changing the Standard to G.1X or G.2X worker type increases memory per executor. A quick memory tip: “DPUs = Distributed Processing Units, so more DPUs means more distributed memory for your data.”
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 process data from Amazon S3. The Glue job reads CSV files and writes Parquet files to a different S3 bucket. The job occasionally fails with 'java.lang.OutOfMemoryError: Java heap space'. The data size varies. Which change should the engineer make to avoid this error?
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.
The 'java.lang.OutOfMemoryError: Java heap space' error in AWS Glue indicates that the Spark executors ran out of memory while processing the data. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory available across the cluster, allowing larger datasets to be processed without hitting the heap limit. Each DPU provides 4 vCPUs and 16 GB of memory, so adding more DPUs scales memory linearly.
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.
✗
Convert the CSV files to JSON format before processing.
Decrease the Spark shuffle partitions in the job script.
Why it's wrong here
This might reduce memory but is less direct and could impact performance.
✗
Increase the job timeout setting.
Why it's wrong here
Timeout only affects job duration, not memory.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'increasing DPUs' with 'increasing parallelism' and assume it only speeds up jobs, but in reality it also increases total memory, which directly mitigates heap space errors.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue runs Apache Spark jobs where each executor's heap is determined by the number of DPUs. The default Spark memory fraction (spark.memory.fraction) is 0.6, meaning only 60% of executor heap is available for execution and storage. When data volume spikes, executors may exceed this limit, causing OOM. Increasing DPUs adds more executors or increases executor memory, but tuning spark.executor.memory or spark.memory.offHeap.enabled can also help in scenarios where data skew or large shuffles are the root cause.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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. — The 'java.lang.OutOfMemoryError: Java heap space' error in AWS Glue indicates that the Spark executors ran out of memory while processing the data. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory available across the cluster, allowing larger datasets to be processed without hitting the heap limit. Each DPU provides 4 vCPUs and 16 GB of memory, so adding more DPUs scales memory linearly.
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.
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 company uses AWS Glue to process CSV files stored in Amazon S3. The data pipeline runs daily, but recently some jobs have failed with a 'MemoryError'. The data volume has grown from 1 GB to 10 GB per day. What is the MOST cost-effective solution to resolve this issue?
medium
A.Change the Glue worker type from Standard to G.2X.
✓ B.Increase the number of DPUs (Data Processing Units) allocated to the Glue job.
C.Convert the CSV files to Parquet format using an S3 batch operation.
D.Migrate the job to Amazon EMR with a larger cluster.
Why B: Option C is correct because increasing the number of DPUs allows Glue to handle larger data volumes without changing the job logic. Option A is wrong because switching to a different file format does not address memory limits. Option B is wrong because moving to Amazon EMR is more complex and costly for a simple ETL job. Option D is wrong because additional memory is provisioned via DPUs, not by changing the worker type to 'G.2X' which is for machine learning.
Variation 2. A company uses AWS Glue to transform data from Amazon S3 into Parquet format. The job fails with an out-of-memory error for large files. Which TWO actions can resolve this issue? (Choose TWO.)
hard
A.Change the input format from CSV to JSON.
✓ B.Increase the number of DPUs allocated to the job.
C.Use the Glue streaming ETL feature.
D.Enable CloudWatch logs for detailed error analysis.
✓ E.Split the input data into smaller files.
Why B: Option A increases memory, Option C increases parallelism. Option B is not relevant since the job reads from S3. Option D is for streaming. Option E is for debugging, not resolving OOM.
Variation 3. A company uses AWS Glue ETL jobs to transform data from Amazon RDS to Amazon S3 daily. The job recently started failing with memory errors. The data volume has grown 3x in the past month. Which change should the data engineer make to resolve the issue?
medium
A.Increase the size of the Amazon RDS instance
B.Switch the Glue job type from Python Shell to Spark
C.Partition the output data in Amazon S3 by date
✓ D.Increase the number of DPUs allocated to the Glue job
Why D: Option D is correct because increasing the number of DPUs (Data Processing Units) in the AWS Glue job provides more memory and compute resources, which can handle larger data volumes. Option A (increasing RDS instance size) may not help if the bottleneck is the Glue job. Option B (switching to Spark) is not directly relevant; Glue already uses Spark. Option C (partitioning S3 output) improves query performance but does not fix memory errors during transformation.
Variation 4. 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?
medium
A.Switch from Spark to Python Shell job type.
B.Implement batch processing with smaller file sizes.
✓ C.Increase the number of DPUs allocated to the Glue job.
D.Use Redshift Spectrum to query data directly from S3.
Why C: Option C is correct because 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.
Last reviewed: Jun 11, 2026
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