DEA-C01 Data Ingestion and Transformation Practice Question
An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parquet files, performs aggregations, and writes the results to Amazon Redshift. The job runs successfully but takes longer than expected. The data volume is increasing. Which design change would MOST improve the job's performance?
⚠ Common exam trap
It's easy for candidates to assume increasing DPUs always increases cost without considering that the job's runtime reduction often lowers total cost, and they mistakenly choose a data format or target change that does not address the core parallelism issue.
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 worker nodes (DPUs) for the job.
Increasing the number of Glue worker nodes (DPUs) directly scales the distributed processing capacity of the ETL job, allowing it to process larger volumes of Parquet data in parallel. This is the most straightforward way to reduce execution time when data volume is growing, as AWS Glue automatically partitions the workload across the additional workers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the aggregated results to a single large file instead of multiple partitions.
Why it's wrong here
Single file reduces parallelism and increases shuffle overhead.
- ✗
Convert the Parquet files to CSV to simplify the schema.
Why it's wrong here
CSV is less efficient than Parquet for columnar storage and compression.
- ✗
Replace the Redshift target with Amazon Redshift Spectrum.
Why it's wrong here
Spectrum is for querying S3, not for loading transformed data into Redshift.
- ✓
Increase the number of Glue worker nodes (DPUs) for the job.
Why this is correct
More workers parallelize tasks and reduce runtime.
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 |
Go deeper
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,711-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.