DEA-C01 Data Store Management Practice Question
A company uses Amazon DynamoDB to store session data for a web application. The application experiences throttling during peak hours. The data engineer needs to reduce throttling. Which THREE actions should the engineer take?
⚠ Common exam trap
Test-takers frequently confuse reactive scaling (auto scaling) or capacity increases with proactive throttling reduction techniques, while the correct answers focus on caching, request distribution, and retry logic that directly reduce the load on the table.
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 DynamoDB Accelerator (DAX) to cache read requests.
DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency and offloads read requests from the DynamoDB table, directly decreasing the number of read capacity units consumed. By caching frequently accessed session data, DAX mitigates throttling during peak hours without requiring changes to the table's provisioned capacity.
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) to cache read requests.
Why this is correct
Reduces read capacity consumption.
- ✗
Increase the provisioned read capacity units.
Why it's wrong here
Does not address write throttling or hot partitions.
- ✓
Implement exponential backoff in the application.
Why this is correct
Reduces retry pressure during throttling.
- ✓
Design the partition key to include a random suffix to distribute writes.
Why this is correct
Prevents hot partitions by distributing writes evenly.
- ✗
Enable auto scaling on the table.
Why it's wrong here
Auto scaling helps but may not react quickly enough; does not fix partition key issues.
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