- A
Change the job type from Python to Spark.
Why wrong: Glue Python jobs are single-threaded; Spark jobs are parallel but still require enough workers.
- B
Use multiple Glue jobs triggered sequentially.
Why wrong: Sequential jobs would not reduce overall runtime.
- C
Increase the RDS instance size to improve read throughput.
Why wrong: Database read throughput may not be the bottleneck; Glue parallelism is key.
- D
Use AWS Glue Spark job with 100 workers.
More workers enable parallelism, reducing runtime.
Question 1,361 of 1,786
Reducing AWS Glue Job Runtime with Spark Parallelism
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 runs a daily batch ETL job using AWS Glue. The job processes 500 GB of data from Amazon RDS to Amazon S3. The job currently uses a single DPU and takes 6 hours to complete. The team wants to reduce runtime to under 1 hour without increasing costs significantly. Which approach should they use?
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
✓
Use AWS Glue Spark job with 100 workers.
Option D is correct because AWS Glue Spark jobs can parallelize data processing across multiple workers, dramatically reducing runtime. With 100 workers, the job can process the 500 GB dataset in parallel, achieving sub-1-hour runtime while keeping costs relatively low since Glue charges per DPU-second and the total DPU-seconds may be similar to the original 6-hour single-DPU job.
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.
- ✗
Change the job type from Python to Spark.
Why it's wrong here
Glue Python jobs are single-threaded; Spark jobs are parallel but still require enough workers.
- ✗
Use multiple Glue jobs triggered sequentially.
Why it's wrong here
Sequential jobs would not reduce overall runtime.
- ✗
Increase the RDS instance size to improve read throughput.
Why it's wrong here
Database read throughput may not be the bottleneck; Glue parallelism is key.
- ✓
Use AWS Glue Spark job with 100 workers.
Why this is correct
More workers enable parallelism, reducing runtime.
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 might think increasing parallelism (Option D) is too expensive, but Glue's pay-per-DPU-second model means a job with 100 workers running for 1 hour costs roughly the same as 1 worker running for 100 hours, so the total cost is similar, not significantly higher.
Detailed technical explanation
How to think about this question
AWS Glue Spark jobs use Apache Spark's distributed computing model, where data is split into partitions processed in parallel across executors. The number of workers (DPUs) directly controls parallelism; with 100 workers, the job can process approximately 100 partitions simultaneously, reducing runtime proportionally. Glue's dynamic allocation can also adjust worker count during execution, but setting a fixed high worker count ensures consistent parallelism for large datasets.
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
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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: Use AWS Glue Spark job with 100 workers. — Option D is correct because AWS Glue Spark jobs can parallelize data processing across multiple workers, dramatically reducing runtime. With 100 workers, the job can process the 500 GB dataset in parallel, achieving sub-1-hour runtime while keeping costs relatively low since Glue charges per DPU-second and the total DPU-seconds may be similar to the original 6-hour single-DPU job.
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
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.
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