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
Error Log:
[ERROR] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 6, ip-10-0-0-12.ec2.internal, executor 1): java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.sql.catalyst.expressions.UnsafeRow.<init>(UnsafeRow.java:42)
Refer to the exhibit. An AWS Glue ETL job is failing with an OutOfMemoryError. The job reads from Amazon S3 and performs a GROUP BY on a large dataset. Which change should the data engineer make to resolve this error?
Exhibit
Error Log:
[ERROR] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 6, ip-10-0-0-12.ec2.internal, executor 1): java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.sql.catalyst.expressions.UnsafeRow.<init>(UnsafeRow.java:42)
A
Use coalesce to reduce the number of partitions.
Why wrong: Coalesce reduces partitions but may cause data skew.
B
Increase the number of DPUs allocated to the Glue job.
More DPUs increase total memory available.
C
Increase the number of partitions in the DataFrame.
Why wrong: More partitions may increase overhead, not reduce memory per task.
D
Use repartition to increase the number of partitions.
Why wrong: Repartition increases partitions but may increase memory usage.
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 OutOfMemoryError in an AWS Glue ETL job performing a GROUP BY on a large dataset indicates that the executors do not have enough memory to handle the shuffle operations required for aggregation. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory and compute resources available, allowing the job to process larger partitions without running out of memory.
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.
✗
Use coalesce to reduce the number of partitions.
Why it's wrong here
Coalesce reduces partitions but may cause data skew.
✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
More DPUs increase total memory available.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of partitions in the DataFrame.
Why it's wrong here
More partitions may increase overhead, not reduce memory per task.
✗
Use repartition to increase the number of partitions.
Why it's wrong here
Repartition increases partitions but may increase memory usage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse partition tuning (coalesce/repartition) with resource allocation, mistakenly thinking that adjusting partitions alone can fix memory errors without increasing the underlying compute and memory capacity.
Detailed technical explanation
How to think about this question
AWS Glue uses Apache Spark under the hood, where each DPU provides 4 vCPUs and 16 GB of memory. The GROUP BY operation triggers a wide transformation (shuffle), which requires data to be exchanged across executors and stored in memory for aggregation. Increasing DPUs scales the cluster horizontally, adding more executors and total memory, which directly addresses the OOM error by providing sufficient memory for shuffle buffers and hash aggregation tables.
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.
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 OutOfMemoryError in an AWS Glue ETL job performing a GROUP BY on a large dataset indicates that the executors do not have enough memory to handle the shuffle operations required for aggregation. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory and compute resources available, allowing the job to process larger partitions without running out of memory.
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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Question Discussion
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