The correct answer is to implement a local cache in each application instance to reduce load on the centralized Redis cluster. This pattern, known as a multi-tier or near-cache, prevents a cache stampede by absorbing repeated read requests for the same uncached profiles directly within the application’s memory, using libraries like Caffeine or Guava. During a traffic spike, the local cache acts as a first line of defense, drastically cutting the number of calls hitting ElastiCache and the database, which avoids the high latency and overload described. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of caching layers beyond simple TTL adjustments—many candidates mistakenly focus on tweaking TTLs or using write-through, but the core issue is request amplification, not expiration timing. A key memory tip: think “local first, central second” to remember that a near-cache shields the shared cache from sudden floods.
DBS-C01 Workload-Specific Database Design Practice Question
This DBS-C01 practice question tests your understanding of workload-specific database design. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
A local cache acts as an L1 cache in front of Redis (L2), using a pattern like cache-aside with a short local TTL (e.g., seconds to minutes) to reduce network round trips and Redis load. Under the hood, this leverages the principle of temporal locality: during a spike, the same hot profiles are requested repeatedly by multiple application instances, and a local cache can serve them without Redis involvement. In real-world scenarios, this pattern is critical for social media platforms where a viral event causes a thundering herd problem, and it can be combined with request coalescing or circuit breakers to further protect downstream resources.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Workload-Specific Database Design — This question tests Workload-Specific Database Design — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement a local cache in each application instance to reduce load on the centralized Redis cluster. — Option B is correct because 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.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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