DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to run a nightly ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3 in Parquet format. The DynamoDB table is large and has a high read capacity. The engineer wants to minimize the impact on the DynamoDB table's performance and reduce the ETL job's runtime. Which approach should the engineer take?
⚠ Common exam trap
The trap here is assuming that using DAX or parallel scans will avoid impacting DynamoDB when they still consume read capacity or add complexity.
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
✓
Export the DynamoDB table to Amazon S3 using DynamoDB's native export feature, then use AWS Glue to read from S3 and write to Parquet.
To minimize impact on DynamoDB performance and reduce ETL runtime, exporting the table to S3 is ideal because it does not consume read capacity. AWS Glue can then process the exported data from S3, which is faster and does not affect the source table. Other methods like parallel scans or increasing workers still consume capacity and can impact performance.
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 AWS Glue with DynamoDB connector and enable DynamoDB Accelerator (DAX) for the table.
Why it's wrong here
DAX is an in-memory cache for DynamoDB that reduces read latency for read-heavy workloads, but it is designed for application reads, not bulk ETL. Enabling DAX does not help Glue read the entire table efficiently; Glue would still consume read capacity. DAX also adds cost and complexity. For bulk reads, using DynamoDB export to S3 is more efficient and does not impact table performance.
- ✗
Use AWS Glue with DynamoDB connector and increase the job's number of workers to read the table faster.
Why it's wrong here
Increasing the number of workers may speed up the Glue job by allowing more parallel reads, but it also increases the read capacity consumption on the DynamoDB table, potentially causing throttling and impacting performance. This approach does not align with the goal of minimizing impact. It also increases cost. The native export feature is a better way to avoid consuming table capacity.
- ✗
Use AWS Glue with DynamoDB connector and configure the job to use parallel scans with a segment ratio.
Why it's wrong here
Parallel scans via segments can speed up reading from DynamoDB, but they still consume read capacity units and can impact table performance. The engineer wants to minimize impact, and parallel scans do not eliminate the consumption of provisioned throughput. They also require careful tuning to avoid throttling. This approach is better than a single scan but not as efficient as exporting the table without consuming capacity.
- ✓
Export the DynamoDB table to Amazon S3 using DynamoDB's native export feature, then use AWS Glue to read from S3 and write to Parquet.
Why this is correct
DynamoDB's native export to S3 does not consume read capacity units and does not affect table performance. The export creates data in DynamoDB JSON format in S3. AWS Glue can then read from S3, transform as needed, and write to Parquet. This approach minimizes impact on the table and can be faster for large tables since it uses a separate export process. It also reduces the ETL job's runtime because Glue reads from S3 instead of DynamoDB.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.