- A
Disable Cluster Mode and use a single node.
Why wrong: Using a single node reduces capacity and can lead to more evictions, worsening the hit ratio.
- B
Implement a consistent hashing algorithm for key distribution.
Consistent hashing minimizes key redistribution when nodes change and ensures even distribution, reducing evictions and improving hit ratio.
- C
Increase the TTL (time-to-live) for all cache keys.
Why wrong: Increasing TTL keeps data longer but does not address the underlying distribution issue; it may lead to stale data.
- D
Use random TTLs to spread out expiration times.
Why wrong: Random TTLs do not address the distribution problem and can cause cache misses at unpredictable times.
Question 1,579 of 1,730
Fixing Cache Hit Ratio Drop with Consistent Hashing
This DBS-C01 practice question tests your understanding of management and operations. 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 company is using Amazon ElastiCache for Redis as a caching layer for a high-traffic web application. The cache hit ratio has dropped from 95% to 70% after a recent deployment that changed the cache key structure. The application team wants to improve the hit ratio without increasing the cache memory. Which action should they take?
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 consistent hashing algorithm for key distribution.
A drop in cache hit ratio after changing the key structure indicates that the new keys are not being distributed evenly across the cluster nodes, causing some nodes to be overloaded with misses. Implementing a consistent hashing algorithm (e.g., using the Redis Cluster hash slot mechanism or a client-side library like libketama) ensures that keys are distributed uniformly across shards, minimizing cache misses and improving the hit ratio without adding memory.
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.
- ✗
Disable Cluster Mode and use a single node.
Why it's wrong here
Using a single node reduces capacity and can lead to more evictions, worsening the hit ratio.
- ✓
Implement a consistent hashing algorithm for key distribution.
Why this is correct
Consistent hashing minimizes key redistribution when nodes change and ensures even distribution, reducing evictions and improving hit ratio.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the TTL (time-to-live) for all cache keys.
Why it's wrong here
Increasing TTL keeps data longer but does not address the underlying distribution issue; it may lead to stale data.
- ✗
Use random TTLs to spread out expiration times.
Why it's wrong here
Random TTLs do not address the distribution problem and can cause cache misses at unpredictable times.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse TTL management (options C and D) with key distribution issues, mistakenly believing that extending or randomizing expiration times will fix a hit ratio drop caused by poor key placement across cluster nodes.
Detailed technical explanation
How to think about this question
In Redis Cluster, keys are distributed across 16,384 hash slots using a CRC16 hash of the key modulo 16384. A consistent hashing algorithm (or proper hash slot assignment) ensures that keys are evenly spread across nodes, preventing 'hot spots' where one node handles a disproportionate number of misses. Without consistent hashing, a change in key naming (e.g., adding a prefix) can skew the hash distribution, causing many keys to land on the same node and increasing cache misses for that node.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Management and Operations — This question tests Management and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement a consistent hashing algorithm for key distribution. — A drop in cache hit ratio after changing the key structure indicates that the new keys are not being distributed evenly across the cluster nodes, causing some nodes to be overloaded with misses. Implementing a consistent hashing algorithm (e.g., using the Redis Cluster hash slot mechanism or a client-side library like libketama) ensures that keys are distributed uniformly across shards, minimizing cache misses and improving the hit ratio without adding memory.
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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Last reviewed: Jul 4, 2026
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