DEA-C01 Data Store Management Practice Question
A company is building a real-time analytics dashboard using Amazon Kinesis Data Streams and Amazon DynamoDB. The data engineer needs to ensure that the DynamoDB table can handle write spikes without throttling. Which approach is the most cost-effective?
⚠ Common exam trap
Watch out — candidates often confuse DAX's read caching with write handling, or assume that auto-scaling provisioned capacity can handle sudden spikes instantly, when in reality it scales gradually and can still throttle during rapid bursts.
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 on-demand capacity mode.
DynamoDB on-demand capacity mode automatically scales to handle write spikes without requiring capacity planning or management, making it the most cost-effective choice for unpredictable workloads like a real-time analytics dashboard. It charges per request, so you only pay for the writes you actually use, avoiding over-provisioning costs.
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 writes.
Why it's wrong here
DAX caches reads, not writes.
- ✗
Use provisioned capacity with auto-scaling set to a maximum of 10,000 WCU.
Why it's wrong here
Auto-scaling may not react fast enough for sudden spikes.
- ✗
Use an Amazon Lambda function to buffer writes and batch them to DynamoDB.
Why it's wrong here
Lambda can buffer, but may still throttle if write capacity is exceeded.
- ✓
Use DynamoDB on-demand capacity mode.
Why this is correct
On-demand scales instantly and is cost-effective for unpredictable workloads.
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