- A
Increase the number of DPUs to 20
Why wrong: Could help but not as targeted as repartitioning.
- B
Repartition the data before the join operation
Optimizes parallelism and reduces shuffling.
- C
Use coalesce to reduce the number of output files
Why wrong: Reduces parallelism, likely making performance worse.
- D
Change the worker type to G.2X
Why wrong: More memory per worker but does not address partition skew.
Question 739 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
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
✓
Repartition the data before the join operation
The default G.1X worker type provides 16 GB of memory and 4 vCPUs per DPU. With 10 DPUs, the job likely has sufficient compute but suffers from data skew or inefficient partitioning during the join. Repartitioning the data before the join ensures that keys are evenly distributed across partitions, reducing shuffle overhead and preventing straggler tasks, which directly improves performance.
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 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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume more DPUs or a larger worker type always speeds up a job, but the DEA-C01 exam tests understanding that shuffle optimization (like repartitioning) is the most impactful fix for join performance issues.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue uses Apache Spark, where joins trigger a full shuffle of both datasets across partitions. Repartitioning with `.repartition()` based on the join key before the join ensures that rows with the same key land in the same executor, minimizing data movement during the shuffle. In real-world scenarios, a skewed join key (e.g., a few user IDs with millions of records) can cause a single executor to handle most of the work; repartitioning with salting or hash-based partitioning mitigates this.
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.
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.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
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: Repartition the data before the join operation — The default G.1X worker type provides 16 GB of memory and 4 vCPUs per DPU. With 10 DPUs, the job likely has sufficient compute but suffers from data skew or inefficient partitioning during the join. Repartitioning the data before the join ensures that keys are evenly distributed across partitions, reducing shuffle overhead and preventing straggler tasks, which directly improves performance.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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