How DynamoDB Accelerator (DAX) Reduces Throttling and Latency for On-Demand Tables
A company uses Amazon DynamoDB with on-demand capacity for a gaming application that experiences unpredictable traffic spikes. The application reads the same set of 'hot' items frequently. Users report high latency during peak hours. Which action would MOST effectively reduce read latency for the hot items?
⚠ Common exam trap
Many candidates confuse throughput capacity (RCUs/WCUs) with latency, assuming that increasing capacity will speed up individual reads, when in fact capacity only controls the rate of requests, not the response time per request.
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 Accelerator (DAX) for the table.
DynamoDB Accelerator (DAX) is an in-memory cache that sits between the application and DynamoDB, providing microsecond read latency for frequently accessed items. Since the application reads the same set of 'hot' items repeatedly, DAX can serve these reads from its cache, bypassing the storage layer and reducing latency during traffic spikes without requiring any table schema changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable DynamoDB Accelerator (DAX) for the table.
Why this is correct
DAX provides an in-memory write-through cache for eventually consistent reads, absorbing repeated access to the same hot items and cutting microsecond-level latency. This directly addresses the unpredictable spikes and repeated hot-item reads that overwhelm on-demand capacity.
- ✗
Switch to provisioned capacity with auto-scaling.
Why it's wrong here
Provisioned capacity with auto-scaling still partitions data by partition key, so repeatedly read hot items remain throttled on a single partition. It is tempting because it addresses unpredictable spikes, and it would be correct when traffic is uneven over time but access is evenly spread across keys.
- ✗
Increase the read capacity units for the table.
Why it's wrong here
On-demand tables ignore manually set read capacity units, so the change has no effect on throughput or latency. It is tempting because raising RCUs is the classic fix for provisioned tables, and it would be correct where a table runs in provisioned mode and throttling, not hot-partition contention, causes the latency.
- ✗
Enable DynamoDB Global Tables for multi-region replication.
Why it's wrong here
Global Tables replicate data across Regions for locality and disaster recovery; they do not reduce latency for hot items within one Region. It is tempting because replication sounds like it spreads load, and it would be correct when users are geographically dispersed and need low-latency reads from nearby Regions.
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