- A
Store intermediate data in HDFS on Amazon EMR
Why wrong: Adds complexity and cost without clear benefit.
- B
Increase the number of DPUs for the job
More DPUs can reduce runtime, improving cost if job runs shorter.
- C
Reduce the number of DPUs to save cost
Why wrong: Would increase runtime, potentially increasing cost.
- D
Use columnar data formats such as Parquet
Improves performance and reduces data scanned.
- E
Partition the data by date or other high-cardinality columns
Reduces the amount of data processed.
Question 1,424 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 company uses AWS Glue to transform data stored in S3. The Glue job runs daily and processes data in the range of hundreds of GB. The data engineer wants to optimize the job for cost and performance. Which THREE actions should be taken? (Choose THREE.)
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 for the job
Option B is correct because increasing the number of DPUs (Data Processing Units) for an AWS Glue job can improve performance by enabling parallel processing of large datasets (hundreds of GB). AWS Glue allocates resources in increments of DPUs, where each DPU provides 4 vCPU and 16 GB of memory; scaling out DPUs reduces execution time, which can lower overall cost if the job runs fewer minutes, balancing cost and 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.
- ✗
Store intermediate data in HDFS on Amazon EMR
Why it's wrong here
Adds complexity and cost without clear benefit.
- ✓
Increase the number of DPUs for the job
Why this is correct
More DPUs can reduce runtime, improving cost if job runs shorter.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the number of DPUs to save cost
Why it's wrong here
Would increase runtime, potentially increasing cost.
- ✓
Use columnar data formats such as Parquet
Why this is correct
Improves performance and reduces data scanned.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Partition the data by date or other high-cardinality columns
Why this is correct
Reduces the amount of data processed.
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 mistakenly think reducing DPUs always saves cost, but AWS Glue bills by DPU-hour, so longer runtimes from fewer DPUs can actually increase cost, and the question explicitly asks for both cost and performance optimization.
Detailed technical explanation
How to think about this question
AWS Glue uses Apache Spark under the hood, and DPUs directly map to Spark executors; increasing DPUs increases the number of concurrent tasks, which is critical for shuffle-heavy operations like joins or aggregations on hundreds of GB. Columnar formats like Parquet enable predicate pushdown and compression (e.g., Snappy), reducing I/O and storage costs, while partitioning by high-cardinality columns (e.g., date) allows Spark to prune partitions during reads, minimizing data scanned. In practice, a job processing 500 GB with 10 DPUs might take 30 minutes, but with 50 DPUs could finish in 6 minutes, often reducing total cost despite higher DPU-hour consumption.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
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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 DPUs for the job — Option B is correct because increasing the number of DPUs (Data Processing Units) for an AWS Glue job can improve performance by enabling parallel processing of large datasets (hundreds of GB). AWS Glue allocates resources in increments of DPUs, where each DPU provides 4 vCPU and 16 GB of memory; scaling out DPUs reduces execution time, which can lower overall cost if the job runs fewer minutes, balancing cost and 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.
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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