DBS-C01 Workload-Specific Database Design Practice Question
A startup is building a mobile app backend using Amazon DynamoDB. They anticipate unpredictable traffic spikes. Which DynamoDB feature should they use to handle the spikes without manual intervention?
⚠ Common exam trap
The DBS-C01 exam often tests the misconception that caching (DAX) or data expiration (TTL) can handle traffic spikes, but the key is that Auto Scaling directly adjusts the provisioned capacity to match demand, while DAX only caches reads and TTL only manages data lifecycle.
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 Auto Scaling for read and write capacity.
DynamoDB Auto Scaling (option B) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, using CloudWatch alarms and the Application Auto Scaling service. This allows the startup to handle unpredictable spikes without manual intervention, as the service will increase capacity during high demand and decrease it during low demand, ensuring consistent performance and cost efficiency.
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 DynamoDB Accelerator (DAX) as a cache layer.
Why it's wrong here
DAX improves read performance but does not handle write spikes or capacity scaling.
- ✓
Enable DynamoDB Auto Scaling for read and write capacity.
Why this is correct
Auto Scaling adjusts capacity based on traffic patterns, handling spikes automatically.
- ✗
Set up a TTL (Time to Live) to automatically expire old items.
Why it's wrong here
TTL helps with storage management, not throughput spikes.
- ✗
Implement DynamoDB Global Tables for multi-region replication.
Why it's wrong here
Global Tables provide disaster recovery and local reads, not capacity scaling.
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