DEA-C01 Data Operations and Support Practice Question
A data team runs a daily AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes results to Amazon S3. The job completes successfully but takes 2 hours longer than expected. The job uses the JDBC connection to Redshift. The Redshift cluster is 4 dc2.large nodes. The Glue job has 10 workers of type G.1X. Which change would MOST likely reduce the job duration?
⚠ Common exam trap
Many exam-takers assume the bottleneck is either Redshift compute (C) or Glue parallelism (D), when in fact the JDBC driver's single-threaded row-by-row fetch is the primary performance limiter.
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 the S3 staging option in the Glue connection to unload data from Redshift to S3 first
The JDBC connection in AWS Glue reads data row-by-row from Redshift, which is slow for large datasets. By enabling the S3 staging option in the Glue connection, the job uses Redshift's UNLOAD command to export data to S3 in parallel, then Glue reads from S3. This bypasses the JDBC bottleneck and leverages Redshift's massively parallel processing (MPP) to export data much faster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Redshift Spectrum to query data directly from S3
Why it's wrong here
Spectrum is for querying external data, not for unloading; the job writes to S3, not reads.
- ✓
Use the S3 staging option in the Glue connection to unload data from Redshift to S3 first
Why this is correct
UNLOAD is parallel and faster than JDBC; Glue can then read from S3.
- ✗
Increase the Redshift cluster size to 8 nodes
Why it's wrong here
More nodes increase scan throughput but JDBC still serializes; UNLOAD is more effective.
- ✗
Increase the number of Glue workers to 20
Why it's wrong here
More workers may not help if the read from Redshift via JDBC is the bottleneck.
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
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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.