DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer must load data from an Amazon DynamoDB table into an Amazon S3 data lake nightly. The table is approximately 800 GB and the nightly window is tight. The engineer wants a fully managed, serverless option that exports the table to S3 without consuming DynamoDB read capacity or writing custom code. Which solution meets these requirements?
⚠ Common exam trap
The trap here is assuming DynamoDB Streams can produce a full historical export, when streams only capture changes that occur after the stream is enabled.
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 native Amazon DynamoDB export to Amazon S3 feature, which exports a point-in-time snapshot without consuming read capacity.
Amazon DynamoDB's native export to Amazon S3 creates a full point-in-time snapshot of the table directly in an S3 bucket without consuming read capacity and without requiring custom code or replication infrastructure. It is serverless and scales to large tables, which fits the tight nightly window and the no-capacity-impact constraint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run an AWS Database Migration Service task with DynamoDB as the source and Amazon S3 as the target on a nightly schedule.
Why it's wrong here
AWS DMS supports DynamoDB as a source, but continuous or full-load replication consumes table read capacity and requires managing replication instances, endpoints, and task configuration. It is not a serverless, code-free export and is heavier to operate than the native snapshot export feature.
- ✓
Use the native Amazon DynamoDB export to Amazon S3 feature, which exports a point-in-time snapshot without consuming read capacity.
Why this is correct
DynamoDB's native export to Amazon S3 produces a full point-in-time snapshot in DynamoDB JSON or Amazon Ion format directly to an S3 bucket. It is serverless, requires no code, and does not consume provisioned or on-demand read capacity, making it ideal for a large nightly export within a tight window.
- ✗
Use AWS Glue with a DynamoDB connection and the dynamodb.read.throughput.percent option set to 100.
Why it's wrong here
A Glue job reading DynamoDB with the throughput percent option consumes provisioned read capacity units from the table, which affects production traffic and requires managing DPU capacity. It also is not a fully managed export mechanism and requires authoring and maintaining ETL code, so it does not satisfy the no-code, no-capacity-impact requirement.
- ✗
Enable DynamoDB Streams, attach an AWS Lambda function, and write each stream record to Amazon S3 as it arrives.
Why it's wrong here
DynamoDB Streams capture only item-level changes made after the stream is enabled, so a full 800 GB table export is not possible this way. Maintaining a Lambda consumer adds operational overhead and code, and the approach cannot produce a consistent point-in-time snapshot of the entire table.
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 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
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