DBS-C01 Workload-Specific Database Design Practice Question
A company uses Amazon DynamoDB for a session management workload. The access pattern is random and requires single-digit millisecond latency. The table has a read capacity of 5000 RCU. During peak hours, read requests occasionally exceed this capacity, causing throttling. Which design change is most appropriate to handle traffic spikes?
⚠ Common exam trap
Test-takers frequently choose DAX (Option D) thinking it solves throttling by caching, but DAX only reduces read load if the same items are frequently requested—random access patterns with low cache hit rates make DAX ineffective for preventing throttling, and it does not increase the table's RCU limit.
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 reads.
DynamoDB auto scaling allows the table to dynamically adjust its provisioned read capacity (RCU) in response to traffic spikes, preventing throttling while maintaining single-digit millisecond latency. This is the most appropriate design change for a session management workload with random access patterns, as it handles occasional bursts without requiring manual intervention or architectural 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.
- ✗
Switch to DynamoDB on-demand mode.
Why it's wrong here
On-demand can handle spikes but may cost more; auto scaling is more cost-effective for predictable patterns.
- ✗
Add a global secondary index with different partition key.
Why it's wrong here
Indexes don't increase base table capacity.
- ✓
Enable DynamoDB auto scaling for reads.
Why this is correct
Dynamically adjusts capacity to handle spikes.
- ✗
Add a DAX cluster to cache read requests.
Why it's wrong here
DAX does not increase write capacity.
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