DEA-C01 Data Store Management Practice Question
A company runs a real-time analytics platform on Amazon ECS that ingests streaming data from Amazon Kinesis Data Streams, processes it, and stores results in Amazon DynamoDB. The data volume spikes unpredictably, causing DynamoDB to throttle write requests. The application uses on-demand capacity mode. The data engineer notices that the throttling occurs on a specific partition due to a hot key. The hot key is a customer ID that receives a disproportionate number of writes. The application cannot change the partition key design immediately. The engineer needs to reduce throttling while maintaining low latency. Which solution is most effective?
⚠ Common exam trap
It's easy for candidates to assume on-demand capacity eliminates all throttling, but it does not protect against hot key skew; they may also confuse DAX's read caching with write buffering, or think retrying throttled writes is a viable solution rather than a reactive fix that increases latency.
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
✓
Implement a write buffer using Amazon SQS, and have consumers write to DynamoDB at a controlled rate.
Buffering writes through Amazon SQS decouples the ingestion rate from DynamoDB's capacity, allowing consumers to write at a controlled pace. This directly mitigates throttling on the hot key without requiring a partition key redesign, and SQS provides low-latency, durable buffering suitable for real-time analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to provisioned capacity with auto scaling and increase the write capacity units.
Why it's wrong here
Provisioned capacity may still throttle during spikes, and setting high capacity is costly.
- ✓
Implement a write buffer using Amazon SQS, and have consumers write to DynamoDB at a controlled rate.
Why this is correct
SQS decouples the producers from the writes, allowing batch processing and reducing throttling.
- ✗
Enable DynamoDB Accelerator (DAX) to cache the hot key writes.
Why it's wrong here
DAX is for reads, not writes.
- ✗
Use DynamoDB Streams to trigger a Lambda function that retries throttled writes.
Why it's wrong here
Retries add latency and may not solve the throttling.
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