DBS-C01 Workload-Specific Database Design Practice Question
A social media startup is using Amazon ElastiCache for Redis to cache user profiles. The cache currently has a 24-hour TTL. The application experiences a sudden spike in traffic after a celebrity mentions the service, causing the cache to be flooded with requests for uncached profiles. This results in high latency and database load. Which design pattern should the company implement to prevent this in the future?
⚠ Common exam trap
Test-takers frequently assume extending TTL or changing cache write strategies (write-through vs. read-through) will solve a cache-miss storm, when in fact the core issue is the volume of concurrent misses, which only a local cache or similar request-reduction pattern can mitigate.
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 local cache in each application instance to reduce load on the centralized Redis cluster.
Implementing a local cache (e.g., using a library like Caffeine or Guava) in each application instance reduces the number of requests hitting the centralized Redis cluster during a traffic spike. This pattern, often called a multi-tier or near-cache, absorbs repeated reads for the same uncached profiles locally, preventing cache flooding and database overload without relying solely on Redis TTL adjustments.
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 a read-through cache with a longer TTL (e.g., 48 hours).
Why it's wrong here
Read-through still causes a thundering herd on the first miss for each item.
- ✓
Implement a local cache in each application instance to reduce load on the centralized Redis cluster.
Why this is correct
Local caching reduces the number of requests to Redis and the database, helping to mitigate cache stampedes.
- ✗
Use a write-through cache with a longer TTL (e.g., 48 hours).
Why it's wrong here
Write-through helps keep cache updated but does not prevent initial misses for uncached items.
- ✗
Use a write-through cache with a shorter TTL (e.g., 1 hour).
Why it's wrong here
Shorter TTL increases cache misses, worsening the problem.
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This DBS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DBS-C01 exam.