DBS-C01 Workload-Specific Database Design Practice Question
A media company uses Amazon ElastiCache for Redis to cache database query results and reduce load on the primary database. The cache hit ratio is low because the cache is purged frequently. The team wants to improve the hit ratio without increasing the cache size. Which strategy should they implement?
⚠ Common exam trap
Test-takers frequently confuse cache eviction (due to memory pressure) with cache expiration (due to TTL), and assume that changing the eviction policy or caching strategy will fix a problem caused by entries being removed too quickly by expiration.
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
✓
Increase the TTL for cached entries to reduce early evictions.
Increasing the TTL (Time-To-Live) for cached entries allows them to remain in the cache longer, reducing the frequency of evictions due to expiration. Since the cache is purged frequently, a low TTL is likely causing entries to expire before they can be reused, which directly lowers the hit ratio. By extending the TTL, the team can retain popular entries longer without needing to increase the cache size, as the existing memory is used more efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the TTL for cached entries to reduce early evictions.
Why this is correct
Longer TTL keeps data in cache longer, improving hit ratio.
- ✗
Implement lazy loading to populate cache only on demand.
Why it's wrong here
Lazy loading does not increase TTL or reduce evictions.
- ✗
Use write-through caching to update cache on every database write.
Why it's wrong here
Write-through ensures data is always fresh but does not extend TTL.
- ✗
Set eviction policy to allkeys-random to spread evictions evenly.
Why it's wrong here
Random eviction may still remove popular keys.
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