- A
Reduce the number of DPUs to minimize overhead.
Why wrong: Fewer DPUs will increase runtime.
- B
Use the Spark UI to analyze bottlenecks and rewrite code.
Why wrong: The question states no change to transformation logic.
- C
Increase the number of Glue DPUs or enable auto-scaling.
More DPUs provide parallel processing and reduce runtime.
- D
Switch from Spark to Python shell.
Why wrong: Python shell is single-node and slower for large data.
Question 1,319 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 is using AWS Glue ETL to transform a large dataset in S3. The job processes 2 TB of data daily and currently runs for 6 hours. The engineer wants to reduce runtime without changing the transformation logic. What is the best approach?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 Glue DPUs or enable auto-scaling.
Increasing the number of DPUs or enabling auto-scaling directly allocates more distributed processing capacity to the AWS Glue job, which reduces runtime for large datasets by parallelizing the workload across more resources. Since the transformation logic is fixed and the job is already running on Spark, adding compute capacity is the most straightforward way to speed up processing without code changes.
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.
- ✗
Reduce the number of DPUs to minimize overhead.
Why it's wrong here
Fewer DPUs will increase runtime.
- ✗
Use the Spark UI to analyze bottlenecks and rewrite code.
Why it's wrong here
The question states no change to transformation logic.
- ✓
Increase the number of Glue DPUs or enable auto-scaling.
Why this is correct
More DPUs provide parallel processing and reduce runtime.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch from Spark to Python shell.
Why it's wrong here
Python shell is single-node and slower for large data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think reducing DPUs reduces overhead and speeds up the job, but in distributed systems, more parallelism (more DPUs) reduces runtime for large datasets, while reducing DPUs increases it.
Detailed technical explanation
How to think about this question
AWS Glue ETL jobs run on Apache Spark, where DPUs (Data Processing Units) represent a unit of compute and memory (4 vCPU, 16 GB memory). Auto-scaling dynamically adjusts the number of DPUs based on workload, optimizing resource usage and runtime. In practice, for a 2 TB daily job, scaling from 10 to 20 DPUs could halve runtime if the workload is CPU-bound and the data is well-partitioned in S3.
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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Data Ingestion and Transformation — study guide chapter
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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: Increase the number of Glue DPUs or enable auto-scaling. — Increasing the number of DPUs or enabling auto-scaling directly allocates more distributed processing capacity to the AWS Glue job, which reduces runtime for large datasets by parallelizing the workload across more resources. Since the transformation logic is fixed and the job is already running on Spark, adding compute capacity is the most straightforward way to speed up processing without code changes.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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