Quick Answer
The correct answer is to repartition the data before the join operation. This directly addresses the root cause of poor performance: partition skew, where uneven data distribution across partitions forces some workers to handle a disproportionate workload while others remain idle. By repartitioning on the join key, you redistribute the data more evenly, maximizing parallelism and reducing straggler tasks. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of how AWS Glue’s Spark-based engine handles data shuffling and skew—a common performance bottleneck in ETL jobs. A frequent trap is assuming more DPUs or a larger worker type (G.2X) will always help, but these are brute-force fixes that don’t solve the underlying imbalance. Remember the mnemonic: “Skew? Re-key and re-partition before the join mission.”
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 data engineer notices that an AWS Glue ETL job is running slower than expected. The job reads from Amazon S3, joins two datasets, and writes the result back to S3. The job uses the default worker type (G.1X) and 10 DPUs. Which action is most likely to improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Increase the number of DPUs to 20
Why wrong: Could help but not as targeted as repartitioning.
Repartition the data before the join operation
Optimizes parallelism and reduces shuffling.
Use coalesce to reduce the number of output files
Why wrong: Reduces parallelism, likely making performance worse.
Change the worker type to G.2X
Why wrong: More memory per worker but does not address partition skew.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Repartition the data before the join operation
Option B is correct because increasing the number of partitions (via repartition) can improve parallelism and performance when the data is skewed. Option A (increasing DPUs) may help but is not the most targeted fix. Option C (using coalesce) reduces partitions, which can hurt performance. Option D (changing to G.2X) provides more memory per worker but may not address the core issue of partition skew.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the number of DPUs to 20
Why it's wrong here
Could help but not as targeted as repartitioning.
Repartition the data before the join operation
Why this is correct
Optimizes parallelism and reduces shuffling.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use coalesce to reduce the number of output files
Why it's wrong here
Reduces parallelism, likely making performance worse.
Change the worker type to G.2X
Why it's wrong here
More memory per worker but does not address partition skew.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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 — study guide chapter
Learn the concepts, then practise the questions
Data Ingestion and Transformation 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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise DEA-C01 questions linked to Data Ingestion and Transformation.
Practise DEA-C01 questions linked to Data Operations and Support.
Practise DEA-C01 questions linked to Data Security and Governance.
Practise DEA-C01 questions linked to Data Store Management.
Practise DEA-C01 questions linked to DEA-C01 fundamentals.
Practise DEA-C01 questions linked to DEA-C01 scenario.
Practise DEA-C01 questions linked to DEA-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
The correct answer is: Repartition the data before the join operation — Option B is correct because increasing the number of partitions (via repartition) can improve parallelism and performance when the data is skewed. Option A (increasing DPUs) may help but is not the most targeted fix. Option C (using coalesce) reduces partitions, which can hurt performance. Option D (changing to G.2X) provides more memory per worker but may not address the core issue of partition skew.
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.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 20, 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.