DEA-C01 Data Store Management Practice Question
A financial services company stores transaction records in an Amazon DynamoDB table. An audit requires that all data older than 7 years be automatically and permanently deleted. The data engineering team must implement this with minimal operational overhead and no application code changes. What should the team do?
⚠ Common exam trap
The trap here is assuming that DynamoDB Streams or point-in-time recovery can enforce time-based retention, when only TTL provides automatic, attribute-driven item expiration.
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
✓
Configure a TTL attribute on the table with an expiry timestamp set to 7 years from the transaction date.
DynamoDB TTL is the native, serverless mechanism for automatic item expiration. By storing an epoch timestamp attribute set to the transaction date plus seven years, DynamoDB deletes expired items in the background without consuming write capacity or requiring custom code. It directly satisfies the audit's permanent deletion requirement with minimal operational effort, unlike stream-based, step-function, or restore-based approaches.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure a TTL attribute on the table with an expiry timestamp set to 7 years from the transaction date.
Why this is correct
DynamoDB Time to Live (TTL) lets you define an attribute holding an expiration timestamp; DynamoDB automatically deletes expired items at no extra cost, with no code or infrastructure to manage. Setting the attribute to transaction date plus 7 years satisfies the audit requirement and requires only a one-time schema and write-path change, not ongoing operations.
- ✗
Enable point-in-time recovery and restore the table to a point before the 7-year window, then delete the original table.
Why it's wrong here
Point-in-time recovery restores a table to any point within the last 35 days, so it cannot target data older than 7 years. Restoring and swapping tables would also cause application downtime and is not an automated retention mechanism. This option misunderstands the PITR retention window and the requirement.
- ✗
Create a scheduled Amazon EventBridge rule that invokes an AWS Step Functions state machine to scan and delete old items.
Why it's wrong here
This approach requires building and maintaining a state machine, scan logic, and error handling, which is significant operational overhead. Scans are also costly on large tables and can consume provisioned capacity. It does not use a native DynamoDB feature for expiration and violates the minimal-overhead requirement.
- ✗
Enable DynamoDB Streams and write an AWS Lambda function that deletes items older than 7 years.
Why it's wrong here
DynamoDB Streams captures item-level changes, not time-based expiry, and a Lambda consumer would need to scan the table periodically to find old items. This adds compute cost and custom code, and a full-table scan on a large table is inefficient. It does not meet the requirement for minimal operational overhead or no application changes.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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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