DEA-C01 Data Store Management Practice Question
A data engineer needs to keep a near-real-time copy of an Amazon DynamoDB table in Amazon S3 for analytics, capturing every item-level change with the before and after images and no impact on table write latency. Which approach meets these requirements with the LEAST operational effort?
⚠ Common exam trap
The trap here is treating point-in-time recovery or scheduled scans as change data capture, when only a stream carries every item modification with before and after images in near real time.
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
✓
Enable DynamoDB Streams on the table and write an AWS Lambda function to batch stream records into S3.
DynamoDB Streams records every item-level change in order and can be configured to include both the old and new images of each item. A Lambda function consuming the stream writes batched records to S3 asynchronously, so table write latency is unaffected, and the serverless subscription keeps operational effort low. The alternative approaches are periodic, snapshot-based, or heavier to operate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable DynamoDB point-in-time recovery and export the recovery window to S3 on a schedule.
Why it's wrong here
Point-in-time recovery continuously backs up the table for restore to any second in the last 35 days, but it is a disaster-recovery feature, not a change-data feed. Exports produce periodic full snapshots, so they miss item-level before and after images and cannot approach near-real-time latency. It also requires scheduled export automation rather than a managed event pipeline.
- ✗
Schedule an AWS Glue job every five minutes to run a full table scan and overwrite the S3 objects.
Why it's wrong here
A periodic full scan is not near-real-time, consumes read capacity on every run, and cannot capture intermediate item states or deletions between scans. Overwriting S3 objects also loses the change history the requirement demands. It adds Glue job maintenance and can interfere with production traffic, so it fails on latency, fidelity, and operational effort.
- ✓
Enable DynamoDB Streams on the table and write an AWS Lambda function to batch stream records into S3.
Why this is correct
DynamoDB Streams captures an ordered log of item-level modifications, and configuring the stream view type to include both new and old images provides the before and after values. A Lambda function subscribed to the stream can batch records into S3 with no servers to manage and no added latency to table writes, since streams are written asynchronously.
- ✗
Use AWS Database Migration Service with change data capture from DynamoDB to Amazon S3.
Why it's wrong here
AWS Database Migration Service supports DynamoDB as a source with change data capture, but it is designed for database migration and replication into relational or streaming targets. Pointing it at Amazon S3 as a target for ongoing item-level analytics adds replication-instance management and configuration overhead compared with a native stream-to-Lambda pipeline, so it demands more operational effort for the same outcome.
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
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