The answer is to change the `maxmemory-policy` to `allkeys-lru`. This configuration directly reduces the eviction rate because it instructs Redis to evict the least recently used keys from the entire keyspace when memory is full, rather than applying a policy that might remove frequently accessed data. In a high-write-volume scenario, this adaptive policy preserves hot data by ensuring only stale, infrequently accessed keys are purged, which also improves the cache hit ratio. On the Google Professional Cloud Database Engineer exam, this question tests your understanding of Redis eviction policies under write-heavy workloads; a common trap is confusing `allkeys-lru` with `volatile-lru`, which only evicts keys with a TTL set, leaving non-expiring keys untouched and potentially causing higher eviction rates. Remember the mnemonic: "All keys, least recently used" — think of it as a global cleanup that prioritizes freshness over expiration.
PCDE Monitor and optimize database performance Practice Question
This PCDE practice question tests your understanding of monitor and optimize database performance. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
You are running a Memorystore for Redis instance with a high write volume. You notice that the eviction rate is high and the cache hit ratio has dropped significantly. Which configuration change would most directly reduce the eviction rate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change maxmemory-policy to allkeys-lru.
Option D is correct because setting `maxmemory-policy` to `allkeys-lru` tells Redis to evict the least recently used keys from the entire keyspace when memory is full. This directly reduces the eviction rate by ensuring that only the least accessed data is removed, preserving the most frequently accessed data and improving the cache hit ratio. In a high-write-volume scenario, this policy adapts to access patterns and minimizes unnecessary evictions of hot data.
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 many small keys instead of a few large keys.
Why it's wrong here
Small keys increase memory overhead due to key storage, potentially increasing evictions.
✗
Decrease the maxmemory setting to force earlier eviction.
Why it's wrong here
Lowering maxmemory would increase eviction rate.
✗
Set maxmemory-policy to noeviction.
Why it's wrong here
This would cause writes to fail instead of evicting keys.
✓
Change maxmemory-policy to allkeys-lru.
Why this is correct
LRU eviction removes least recently used keys, which can improve cache efficiency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'reducing eviction rate' with 'preventing eviction entirely' and choose `noeviction`, not realizing that `noeviction` causes write failures instead of reducing evictions, while `allkeys-lru` actively manages memory to keep evictions low by targeting only cold keys.
Detailed technical explanation
How to think about this question
The `allkeys-lru` policy uses an approximation of the LRU algorithm (Redis does not track true LRU due to performance constraints; it samples a subset of keys, typically 5 by default, and evicts the oldest among them). This sampling approach balances CPU overhead with eviction accuracy. In a real-world scenario with a high write volume, using `allkeys-lru` ensures that even if new keys are constantly added, the least recently used keys (which are likely cold data) are evicted first, maintaining a high cache hit ratio for active workloads.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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.
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change maxmemory-policy to allkeys-lru. — Option D is correct because setting `maxmemory-policy` to `allkeys-lru` tells Redis to evict the least recently used keys from the entire keyspace when memory is full. This directly reduces the eviction rate by ensuring that only the least accessed data is removed, preserving the most frequently accessed data and improving the cache hit ratio. In a high-write-volume scenario, this policy adapts to access patterns and minimizes unnecessary evictions of hot data.
What should I do if I get this PCDE 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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