DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is troubleshooting a slow-running AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job processes 500 GB of CSV data daily. The engineer wants to improve performance. Which THREE actions should the engineer take? (Choose three.)
⚠ Common exam trap
Many exam-takers assume combining files always improves performance (due to Hadoop's small file problem), but in Glue ETL with Spark, moderate parallelism from many files is beneficial, and the real bottleneck is often the JDBC write path, not the S3 read path.
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 a JDBC connection with a higher batch size for writing to Redshift.
Increasing the JDBC batch size for the Redshift connection reduces the number of network round trips and improves write throughput. The Glue JDBC connector batches rows into a single INSERT statement; a larger batch size (e.g., 1000 instead of the default 100) allows more rows per commit, reducing overhead and speeding up the write phase.
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 a JDBC connection with a higher batch size for writing to Redshift.
Why this is correct
Larger batch sizes reduce round trips and improve write throughput.
- ✓
Partition the input data in S3 by date or category.
Why this is correct
Partitioning allows Glue to read only relevant data.
- ✗
Switch to a single-node Redshift cluster to reduce latency.
Why it's wrong here
Single-node reduces parallelism and may worsen performance.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
More DPUs provide more parallelism for processing.
- ✗
Reduce the number of input files by combining them into larger files.
Why it's wrong here
Fewer files can reduce parallelism.
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.