- A
Increase the number of DPUs and set 'spark.sql.shuffle.partitions' to a higher value.
More DPUs and shuffle partitions distribute data across more executors, reducing per-executor memory load.
- B
Increase the number of DPUs and set 'coalesce(1)' in the script.
Why wrong: coalesce(1) reduces parallelism, increasing memory pressure on a single executor.
- C
Decrease the number of DPUs and increase 'spark.shuffle.partitions'.
Why wrong: Decreasing DPUs reduces available memory, worsening the problem.
- D
Set the 'RedshiftTempDir' parameter to a larger S3 bucket.
Why wrong: RedshiftTempDir is for staging data, not for resolving memory errors in Glue.
- E
Set the 'groupFiles' option to 'inPartition' in the S3 source configuration.
groupFiles combines small files into larger partitions, reducing overhead and memory pressure.
Quick Answer
The correct actions are to increase the number of DPUs and to set 'spark.sql.shuffle.partitions' to a higher value. Increasing DPUs directly addresses the MemoryError by allocating more memory and compute capacity to the Glue ETL job, while raising the shuffle partitions reduces the data volume each partition must handle during wide transformations like joins or aggregations, preventing out-of-memory crashes. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Glue’s resource tuning and Spark’s shuffle behavior—a common trap is to only scale DPUs without adjusting shuffle partitions, which can still cause memory pressure. Remember the mnemonic “More DPUs, More Partitions” to fix Glue MemoryError DPU shuffle partitions efficiently.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 data engineer is troubleshooting a Glue ETL job that reads from an S3 bucket and writes to a Redshift table. The job fails with a 'MemoryError' when processing a large dataset. Which TWO actions should the engineer take to resolve this issue? (Choose TWO.)
Answer choices
Why each option matters
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 and set 'spark.sql.shuffle.partitions' to a higher value.
Option A is correct because increasing the number of DPUs (Data Processing Units) provides more memory and compute resources to the Glue job, directly addressing the MemoryError. Setting 'spark.sql.shuffle.partitions' to a higher value reduces the amount of data shuffled per partition, preventing out-of-memory errors during wide transformations like joins or aggregations.
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 and set 'spark.sql.shuffle.partitions' to a higher value.
Why this is correct
More DPUs and shuffle partitions distribute data across more executors, reducing per-executor memory load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of DPUs and set 'coalesce(1)' in the script.
Why it's wrong here
coalesce(1) reduces parallelism, increasing memory pressure on a single executor.
- ✗
Decrease the number of DPUs and increase 'spark.shuffle.partitions'.
Why it's wrong here
Decreasing DPUs reduces available memory, worsening the problem.
- ✗
Set the 'RedshiftTempDir' parameter to a larger S3 bucket.
Why it's wrong here
RedshiftTempDir is for staging data, not for resolving memory errors in Glue.
- ✓
Set the 'groupFiles' option to 'inPartition' in the S3 source configuration.
Why this is correct
groupFiles combines small files into larger partitions, reducing overhead and memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'coalesce(1)' (which reduces parallelism) with a memory-saving technique, or mistakenly think decreasing DPUs or adjusting RedshiftTempDir can fix memory errors, when in fact memory errors require more resources and better partition management.
Detailed technical explanation
How to think about this question
Under the hood, Glue ETL jobs run on Apache Spark, where memory errors often stem from insufficient executor memory or excessive shuffle data. Increasing DPUs adds more executors, while tuning 'spark.sql.shuffle.partitions' (default 200) to a higher value (e.g., 400-800) reduces the size of each shuffle block, preventing spill-to-disk or OOM. In real-world scenarios, a job processing 100 GB of data with default partitions may fail, but increasing partitions to 500 and DPUs to 10 often resolves the issue.
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 Operations and Support — study guide chapter
Learn the concepts, then practise the questions
- →
Data Operations and Support practice questions
Targeted practice on this topic area only
- →
All DEA-C01 questions
1,786 questions across all exam domains
- →
AWS Certified Data Engineer Associate DEA-C01 study guide
Full concept coverage aligned to exam objectives
- →
DEA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DEA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Ingestion and Transformation practice questions
Practise DEA-C01 questions linked to Data Ingestion and Transformation.
Data Operations and Support practice questions
Practise DEA-C01 questions linked to Data Operations and Support.
Data Security and Governance practice questions
Practise DEA-C01 questions linked to Data Security and Governance.
Data Store Management practice questions
Practise DEA-C01 questions linked to Data Store Management.
DEA-C01 fundamentals practice questions
Practise DEA-C01 questions linked to DEA-C01 fundamentals.
DEA-C01 scenario practice questions
Practise DEA-C01 questions linked to DEA-C01 scenario.
DEA-C01 troubleshooting practice questions
Practise DEA-C01 questions linked to DEA-C01 troubleshooting.
Practice this exam
Start a free DEA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — 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 and set 'spark.sql.shuffle.partitions' to a higher value. — Option A is correct because increasing the number of DPUs (Data Processing Units) provides more memory and compute resources to the Glue job, directly addressing the MemoryError. Setting 'spark.sql.shuffle.partitions' to a higher value reduces the amount of data shuffled per partition, preventing out-of-memory errors during wide transformations like joins or aggregations.
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
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 →
Last reviewed: Jun 11, 2026
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.