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?
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
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.
The error indicates that the Glue job is running out of memory during the shuffle phase, which is common when processing large datasets with transformations that require data redistribution. Changing the worker type to G.2X doubles the memory per worker (from 16 GB to 32 GB) without increasing the number of workers, providing the necessary memory headroom for the shuffle operation at a minimal cost increase compared to scaling out with more workers.
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
The trap here is that candidates assume scaling out (more workers) is always the cheapest fix, but increasing worker memory (scaling up) is often more cost-effective for memory-bound shuffle operations because it avoids the overhead of additional task serialization and network shuffling.
Detailed technical explanation
How to think about this question
AWS Glue uses Apache Spark under the hood, and the G.1X worker type allocates 16 GB of memory and 4 vCPUs per DPU. The shuffle phase in Spark spills data to disk when memory is insufficient, but if the data volume exceeds both memory and disk buffer capacity, the job fails with an out-of-memory error. G.2X workers double the memory to 32 GB per DPU, which is often the sweet spot for memory-intensive transformations like aggregations or joins on large datasets without scaling out horizontally.
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
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: Change the worker type to G.2X with 10 workers. — The error indicates that the Glue job is running out of memory during the shuffle phase, which is common when processing large datasets with transformations that require data redistribution. Changing the worker type to G.2X doubles the memory per worker (from 16 GB to 32 GB) without increasing the number of workers, providing the necessary memory headroom for the shuffle operation at a minimal cost increase compared to scaling out with more workers.
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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