The answer is to increase DPUs, use a larger worker type like G.2X, and optimize transformation logic. These three actions directly resolve out-of-memory errors by either allocating more memory per worker or reducing the memory footprint of the job itself. Increasing the number of DPUs adds total compute and memory capacity, while switching to a larger worker type like G.2X boosts memory per executor, which is critical when processing skewed or large datasets. Optimizing the transformation logic—such as avoiding unnecessary shuffles or using broadcast joins—reduces peak memory consumption. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of Glue resource tuning versus code-level fixes; a common trap is choosing Spark’s coalesce, which reduces partitions but doesn’t add memory. Remember the mnemonic “DWO” for DPUs, Worker type, and Optimization to recall the three correct levers for fixing Glue out-of-memory errors.
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 run ETL jobs that transform data from S3 to Redshift. The jobs are failing intermittently with out-of-memory errors. Which THREE actions can help resolve this issue? (Choose THREE.)
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
Options A, B, and D are correct. A: Increasing the number of DPUs provides more memory. B: Using a larger worker type (e.g., G.1X or G.2X) increases memory per worker. D: Optimizing the transformation logic to reduce memory usage helps. C: Using Spark's 'coalesce' reduces partitions but may not solve memory issues. E: Using S3 Select pushes down filtering but does not address 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.
✓
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.
✗
Use S3 Select to filter data before reading into the Glue job
Why it's wrong here
S3 Select pushes down filtering but does not directly resolve out-of-memory errors.
✗
Use Spark's 'coalesce' function to reduce the number of partitions
Why it's wrong here
Coalesce may not free memory; it can even cause out-of-memory if data skew exists.
✓
Optimize the transformation logic to use less memory, for example by filtering early
Why this is correct
Reducing data volume early reduces memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a larger worker type, such as G.2X
Why this is correct
Larger worker types have more memory per worker.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 — Options A, B, and D are correct. A: Increasing the number of DPUs provides more memory. B: Using a larger worker type (e.g., G.1X or G.2X) increases memory per worker. D: Optimizing the transformation logic to reduce memory usage helps. C: Using Spark's 'coalesce' reduces partitions but may not solve memory issues. E: Using S3 Select pushes down filtering but does not address memory.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 run ETL jobs that process data from Amazon S3 and load into Amazon Redshift. The jobs have recently started failing with 'Out of Memory' errors. The data volume has increased 3x in the past month. Which is the MOST effective solution to resolve this issue without redesigning the job?
hard
A.Use Amazon Athena instead of Glue for the transformation.
✓ B.Increase the number of Glue workers (DPUs) for the job.
C.Rewrite the job to use Spark SQL instead of PySpark.
D.Increase the number of partitions in the input S3 data.
Why B: Option A is correct because increasing the number of workers (DPUs) provides more memory and processing capacity. Option B (increasing S3 partitions) may help with parallelism but not directly with memory. Option C (using Spark SQL) is not a direct fix. Option D (switching to Athena) changes the architecture.
Variation 2. A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. The team wants to resolve this issue without increasing costs significantly. Which TWO actions should the team take?
medium
✓ A.Increase the Spark memory overhead parameter in the Glue job configuration.
B.Use DynamicFrame instead of Spark DataFrame for transformations.
C.Increase the number of workers to maximum allowed.
D.Switch from a Spark job to a Python shell job.
✓ E.Change the worker type from 'G.1x' to 'G.2x' to double memory per worker.
Why A: The correct answers are A and C. Increasing Spark memory overhead per worker (option A) provides more memory for Spark operations. Using the 'g.2x' worker type (option C) offers more memory per worker compared to 'g.1x' without doubling cost. Option B (increasing number of workers) increases cost linearly. Option D (using Python shell) is not suitable for large data. Option E (using DynamicFrame) does not address memory.
Last reviewed: Jun 20, 2026
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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.
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