The answer is to increase the number of G.1X workers in the Glue job configuration. This is the most cost-effective fix because it adds parallelism by distributing the data processing across more worker nodes, directly resolving out of memory errors without altering the transformation logic. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of Glue’s scaling model: adding workers (DPUs) increases concurrency, while upgrading to a larger worker type (like G.2X) is more expensive and often unnecessary. A common trap is choosing a larger worker type, which wastes cost when simply adding workers suffices. Remember the memory tip: “More hands, not bigger hands”—scale horizontally with workers before scaling vertically with instance size.
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 uses AWS Glue ETL jobs to transform data and load it into Amazon Redshift. The jobs are failing with 'Out of Memory' errors. What is the most cost-effective way to resolve this issue without changing the transformation logic?
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 G.1X workers in the Glue job configuration.
Option A is correct. Increasing the number of workers (DPUs) adds parallelism without changing logic. Option B is wrong because increasing worker type is more expensive than adding workers. Option C is wrong because a different engine may not be compatible. Option D is wrong because Redshift Spectrum is for querying S3, not for ETL memory.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 G.1X workers in the Glue job configuration.
Why this is correct
More workers increase parallelism and total memory.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use Amazon Redshift Spectrum to query data directly from S3 without transformation.
Why it's wrong here
Redshifting Spectrum bypasses transformation, which may not meet requirements.
✗
Change the worker type to G.2X and keep the same number of workers.
Why it's wrong here
Larger workers are more expensive per unit of compute.
✗
Switch the job from Python to Scala.
Why it's wrong here
Changing language does not address OOM; Scala may use similar memory.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Similar concept trap
Changing language does not address OOM; Scala may use similar memory.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Increase the number of G.1X workers in the Glue job configuration. — Option A is correct. Increasing the number of workers (DPUs) adds parallelism without changing logic. Option B is wrong because increasing worker type is more expensive than adding workers. Option C is wrong because a different engine may not be compatible. Option D is wrong because Redshift Spectrum is for querying S3, not for ETL memory.
What should I do if I get this DEA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 runs a data pipeline using AWS Glue ETL jobs to process daily files from an S3 bucket. The files are in CSV format and range from 1 GB to 10 GB. The Glue job runs successfully for small files but fails with an 'Out of Memory' error for files larger than 5 GB. The job uses a single G.1X DPU (16 GB memory). The company needs to process these large files without changing the existing ETL script. Which solution should the company implement?
hard
A.Convert the input files from CSV to Parquet format to reduce memory usage.
B.Use the Optimus format in AWS Glue to compress data.
C.Use Amazon EMR with Spark instead of AWS Glue.
✓ D.Increase the number of DPUs and use the G.2X worker type to provide more memory per worker.
Why D: Option A is correct because increasing the DPU count and using G.2X worker type provides more memory per worker, resolving the memory issue without script changes. Option B is wrong because converting to Parquet may reduce data size but does not guarantee memory issues are resolved and may require script changes. Option C is wrong because using a different file format may not address memory issues. Option D is wrong because using Spark on EMR requires rewriting the script.
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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.
Question Discussion
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