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
Writing one large file serialises the write and removes parallelism, so Redshift loads and Glue workers cannot proceed concurrently. It is tempting because fewer files appear tidier, and consolidating small files would help when the target suffers from the small-file problem rather than needing parallel writes.
- ✗
Convert the Parquet files to CSV to simplify the schema.
Why it's wrong here
CSV is row-based and uncompressed, so Glue reads far more bytes and cannot push down columns or predicates as Parquet allows. It is tempting because CSV is human-readable and easy to inspect, and would be correct when downstream tools cannot parse columnar formats.
- ✗
Replace the Redshift target with Amazon Redshift Spectrum.
Why it's wrong here
Redshift Spectrum queries external S3 data rather than storing it in Redshift, so it cannot replace the write target and adds per-query scanning overhead. It is tempting because Spectrum offloads storage, and would be right when querying infrequently accessed S3 data without loading it.
- ✓
Increase the number of Glue worker nodes (DPUs) for the job.
Why this is correct
Adding DPUs increases the number of executors available to process partitions in parallel, directly addressing the growing data volume that is stretching job duration. Glue scales horizontally, so more workers reduce per-node workload and shorten the aggregation and write phases.
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 |
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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.